{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 案例数据背景介绍"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.数据来源：\n",
    "\n",
    "     Dongjin Cho，韩国蔚山国立科技学院城市与环境工程学院\n",
    "\n",
    "     Cheolhee Yoo，韩国蔚山国立科技学院城市与环境工程学院\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.摘要：\n",
    "\n",
    "    本案例所使用的数据集包含了韩国首尔2013至2017年夏天的14个天气预报（NWP）的气象预报数据、2个现场观测数据和5个地理辅助变量。\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.数据集信息：\n",
    "\n",
    "    这些数据是为了修正韩国气象局在首尔运行的LDAPS(Local Data Assimilation and Prediction System)模式下次日最高和最低气温预报的偏差。该数据包括2013年至2017年的夏季数据。输入数据主要由LDAPS模型的次日预报数据、当前的现场最高和最低气温以及地理辅助变量组成。此数据有两个输出数据（即第二天的最高和最低气温）；2015年至2017年期间进行了后期验证。\n",
    "    关于LDAPS模式: http://qikan.camscma.cn/jamsweb/cn/article/doi/10.11898/1001-7313.20200103?viewType=HTML\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4.数据属性介绍：\n",
    "\n",
    "      1. station - 气象站编号: 1 - 25 \n",
    "      \n",
    "      2. Date - 记录日期: yyyy-mm-dd ('2013-06-30' to '2017-08-30') \n",
    "      \n",
    "      3. Present_Tmax - 记录日期的0-21时最高气温(Â°C): 20 - 37.6 \n",
    "      \n",
    "      4. Present_Tmin - 记录日期的0-21时最低气温(Â°C): 11.3 - 29.9 \n",
    "      \n",
    "      5. LDAPS_RHmin - LDAPSM模式预测的次日最小相对湿度 (%): 19.8 - 98.5 \n",
    "      \n",
    "      6. LDAPS_RHmax - LDAPS模式预测的次日最大相对湿度 (%): 58.9 - 100 \n",
    "      \n",
    "      7. LDAPS_Tmax_lapse - LDAPS模式预测的次日最高气温递减率 (Â°C): 17.6 - 38.5 \n",
    "      \n",
    "      8. LDAPS_Tmin_lapse - LDAPS模式预测的次日最高气温递减率 (Â°C): 14.3 - 29.6 \n",
    "      \n",
    "      9. LDAPS_WS - LDAPS 模式预测的次日平均风速 (m/s): 2.9 to 21.9 \n",
    "      \n",
    "      10. LDAPS_LH - LDAPS模式预测的次日平均潜热通量 (W/m2): -13.6 - 213.4 \n",
    "      \n",
    "      11. LDAPS_CC1 - LDAPS模式预测的次日第一个6小时平均云量 (0-5 h) (%): 0 - 0.97 \n",
    "      \n",
    "      12. LDAPS_CC2 - LDAPS模式预测的次日第二个6小时平均云量 (0-5 h) (6-11 h) (%): 0 to 0.97 \n",
    "      \n",
    "      13. LDAPS_CC3 - LDAPS模式预测的次日第三个6小时平均云量 (0-5 h) (12-17 h) (%): 0 to 0.98 \n",
    "      \n",
    "      14. LDAPS_CC4 - LDAPS模式预测的次日第四个6小时平均云量 (0-5 h) (18-23 h) (%): 0 to 0.97 \n",
    "      \n",
    "      15. LDAPS_PPT1 - LDAPS模式预测的次日第一个6小时平均降水量 (0-5 h) (%): 0 to 23.7 \n",
    "      \n",
    "      16. LDAPS_PPT2 - LDAPS模式预测的次日第二个6小时平均降水量  (6-11 h) (%): 0 to 21.6 \n",
    "      \n",
    "      17. LDAPS_PPT3 - LDAPS模式预测的次日第三个6小时平均降水量 (12-17 h) (%): 0 to 15.8 \n",
    "      \n",
    "      18. LDAPS_PPT4 - LDAPS模式预测的次日第四个6小时平均降水量  (18-23 h) (%): 0 to 16.7 \n",
    "      \n",
    "      19. lat - 维度 (Â°): 37.456 - 37.645 \n",
    "      \n",
    "      20. lon - 经度 (Â°): 126.826 - 127.135 \n",
    "      \n",
    "      21. DEM - 高程 (m): 12.4 - 212.3 \n",
    "      \n",
    "      22. Slope - 坡度 (Â°): 0.1 - 5.2 \n",
    "      \n",
    "      23. Solar radiation - 太阳辐射量 (wh/m2): 4329.5 to 5992.9 \n",
    "      \n",
    "      24. Next_Tmax - 次日最高气温 (Â°C): 17.4 to 38.9 \n",
    "      \n",
    "      25. Next_Tmin - 次日最低气温 (Â°C): 11.3 to 29.8\n",
    "      \n",
    "\n"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**5. sklearn中常见的几个线性回归类库：**\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:21:36.589506Z",
     "start_time": "2020-06-16T01:21:34.243221Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import DataFrame,Series\n",
    "import pandas_profiling as pp\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "\n",
    "#全部行都能输出\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:21:36.939352Z",
     "start_time": "2020-06-16T01:21:36.858414Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station</th>\n",
       "      <th>Date</th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>Present_Tmin</th>\n",
       "      <th>LDAPS_RHmin</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_Tmin_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>...</th>\n",
       "      <th>LDAPS_PPT2</th>\n",
       "      <th>LDAPS_PPT3</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "      <th>Next_Tmax</th>\n",
       "      <th>Next_Tmin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-06-30</td>\n",
       "      <td>28.7</td>\n",
       "      <td>21.4</td>\n",
       "      <td>58.255688</td>\n",
       "      <td>91.116364</td>\n",
       "      <td>28.074101</td>\n",
       "      <td>23.006936</td>\n",
       "      <td>6.818887</td>\n",
       "      <td>69.451805</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6046</td>\n",
       "      <td>126.991</td>\n",
       "      <td>212.3350</td>\n",
       "      <td>2.7850</td>\n",
       "      <td>5992.895996</td>\n",
       "      <td>29.1</td>\n",
       "      <td>21.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2013-06-30</td>\n",
       "      <td>31.9</td>\n",
       "      <td>21.6</td>\n",
       "      <td>52.263397</td>\n",
       "      <td>90.604721</td>\n",
       "      <td>29.850689</td>\n",
       "      <td>24.035009</td>\n",
       "      <td>5.691890</td>\n",
       "      <td>51.937448</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6046</td>\n",
       "      <td>127.032</td>\n",
       "      <td>44.7624</td>\n",
       "      <td>0.5141</td>\n",
       "      <td>5869.312500</td>\n",
       "      <td>30.5</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2013-06-30</td>\n",
       "      <td>31.6</td>\n",
       "      <td>23.3</td>\n",
       "      <td>48.690479</td>\n",
       "      <td>83.973587</td>\n",
       "      <td>30.091292</td>\n",
       "      <td>24.565633</td>\n",
       "      <td>6.138224</td>\n",
       "      <td>20.573050</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.5776</td>\n",
       "      <td>127.058</td>\n",
       "      <td>33.3068</td>\n",
       "      <td>0.2661</td>\n",
       "      <td>5863.555664</td>\n",
       "      <td>31.1</td>\n",
       "      <td>23.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2013-06-30</td>\n",
       "      <td>32.0</td>\n",
       "      <td>23.4</td>\n",
       "      <td>58.239788</td>\n",
       "      <td>96.483688</td>\n",
       "      <td>29.704629</td>\n",
       "      <td>23.326177</td>\n",
       "      <td>5.650050</td>\n",
       "      <td>65.727144</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6450</td>\n",
       "      <td>127.022</td>\n",
       "      <td>45.7160</td>\n",
       "      <td>2.5348</td>\n",
       "      <td>5856.964844</td>\n",
       "      <td>31.7</td>\n",
       "      <td>24.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2013-06-30</td>\n",
       "      <td>31.4</td>\n",
       "      <td>21.9</td>\n",
       "      <td>56.174095</td>\n",
       "      <td>90.155128</td>\n",
       "      <td>29.113934</td>\n",
       "      <td>23.486480</td>\n",
       "      <td>5.735004</td>\n",
       "      <td>107.965535</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.5507</td>\n",
       "      <td>127.135</td>\n",
       "      <td>35.0380</td>\n",
       "      <td>0.5055</td>\n",
       "      <td>5859.552246</td>\n",
       "      <td>31.2</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   station       Date  Present_Tmax  Present_Tmin  LDAPS_RHmin  LDAPS_RHmax  \\\n",
       "0      1.0 2013-06-30          28.7          21.4    58.255688    91.116364   \n",
       "1      2.0 2013-06-30          31.9          21.6    52.263397    90.604721   \n",
       "2      3.0 2013-06-30          31.6          23.3    48.690479    83.973587   \n",
       "3      4.0 2013-06-30          32.0          23.4    58.239788    96.483688   \n",
       "4      5.0 2013-06-30          31.4          21.9    56.174095    90.155128   \n",
       "\n",
       "   LDAPS_Tmax_lapse  LDAPS_Tmin_lapse  LDAPS_WS    LDAPS_LH  ...  LDAPS_PPT2  \\\n",
       "0         28.074101         23.006936  6.818887   69.451805  ...         0.0   \n",
       "1         29.850689         24.035009  5.691890   51.937448  ...         0.0   \n",
       "2         30.091292         24.565633  6.138224   20.573050  ...         0.0   \n",
       "3         29.704629         23.326177  5.650050   65.727144  ...         0.0   \n",
       "4         29.113934         23.486480  5.735004  107.965535  ...         0.0   \n",
       "\n",
       "   LDAPS_PPT3  LDAPS_PPT4      lat      lon       DEM   Slope  \\\n",
       "0         0.0         0.0  37.6046  126.991  212.3350  2.7850   \n",
       "1         0.0         0.0  37.6046  127.032   44.7624  0.5141   \n",
       "2         0.0         0.0  37.5776  127.058   33.3068  0.2661   \n",
       "3         0.0         0.0  37.6450  127.022   45.7160  2.5348   \n",
       "4         0.0         0.0  37.5507  127.135   35.0380  0.5055   \n",
       "\n",
       "   Solar radiation  Next_Tmax  Next_Tmin  \n",
       "0      5992.895996       29.1       21.2  \n",
       "1      5869.312500       30.5       22.5  \n",
       "2      5863.555664       31.1       23.9  \n",
       "3      5856.964844       31.7       24.3  \n",
       "4      5859.552246       31.2       22.5  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data = open(r'I:/拉钩数据分析实训营/数据分析实战训练营/8数据挖掘算法与实战/3.数据挖掘项目综合实战/作业/阶段三/Dataset.csv')\n",
    "data = pd.read_csv(Data,parse_dates = ['Date'])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据探索"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 基本统计信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:12:45.892284Z",
     "start_time": "2020-06-16T00:12:45.810809Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>station</th>\n",
       "      <td>7750.0</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>7.211568</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Present_Tmax</th>\n",
       "      <td>7682.0</td>\n",
       "      <td>29.768211</td>\n",
       "      <td>2.969999</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>27.800000</td>\n",
       "      <td>29.900000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>37.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Present_Tmin</th>\n",
       "      <td>7682.0</td>\n",
       "      <td>23.225059</td>\n",
       "      <td>2.413961</td>\n",
       "      <td>11.300000</td>\n",
       "      <td>21.700000</td>\n",
       "      <td>23.400000</td>\n",
       "      <td>24.900000</td>\n",
       "      <td>29.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_RHmin</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>56.759372</td>\n",
       "      <td>14.668111</td>\n",
       "      <td>19.794666</td>\n",
       "      <td>45.963543</td>\n",
       "      <td>55.039024</td>\n",
       "      <td>67.190056</td>\n",
       "      <td>98.524734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>88.374804</td>\n",
       "      <td>7.192004</td>\n",
       "      <td>58.936283</td>\n",
       "      <td>84.222862</td>\n",
       "      <td>89.793480</td>\n",
       "      <td>93.743629</td>\n",
       "      <td>100.000153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>29.613447</td>\n",
       "      <td>2.947191</td>\n",
       "      <td>17.624954</td>\n",
       "      <td>27.673499</td>\n",
       "      <td>29.703426</td>\n",
       "      <td>31.710450</td>\n",
       "      <td>38.542255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_Tmin_lapse</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>23.512589</td>\n",
       "      <td>2.345347</td>\n",
       "      <td>14.272646</td>\n",
       "      <td>22.089739</td>\n",
       "      <td>23.760199</td>\n",
       "      <td>25.152909</td>\n",
       "      <td>29.619342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>7.097875</td>\n",
       "      <td>2.183836</td>\n",
       "      <td>2.882580</td>\n",
       "      <td>5.678705</td>\n",
       "      <td>6.547470</td>\n",
       "      <td>8.032276</td>\n",
       "      <td>21.857621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>62.505019</td>\n",
       "      <td>33.730589</td>\n",
       "      <td>-13.603212</td>\n",
       "      <td>37.266753</td>\n",
       "      <td>56.865482</td>\n",
       "      <td>84.223616</td>\n",
       "      <td>213.414006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.368774</td>\n",
       "      <td>0.262458</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.146654</td>\n",
       "      <td>0.315697</td>\n",
       "      <td>0.575489</td>\n",
       "      <td>0.967277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.356080</td>\n",
       "      <td>0.258061</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.140615</td>\n",
       "      <td>0.312421</td>\n",
       "      <td>0.558694</td>\n",
       "      <td>0.968353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.318404</td>\n",
       "      <td>0.250362</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.101388</td>\n",
       "      <td>0.262555</td>\n",
       "      <td>0.496703</td>\n",
       "      <td>0.983789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.299191</td>\n",
       "      <td>0.254348</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.081532</td>\n",
       "      <td>0.227664</td>\n",
       "      <td>0.499489</td>\n",
       "      <td>0.974710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.591995</td>\n",
       "      <td>1.945768</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.052525</td>\n",
       "      <td>23.701544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_PPT2</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.485003</td>\n",
       "      <td>1.762807</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.018364</td>\n",
       "      <td>21.621661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_PPT3</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.278200</td>\n",
       "      <td>1.161809</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007896</td>\n",
       "      <td>15.841235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.269407</td>\n",
       "      <td>1.206214</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>16.655469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lat</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>37.544722</td>\n",
       "      <td>0.050352</td>\n",
       "      <td>37.456200</td>\n",
       "      <td>37.510200</td>\n",
       "      <td>37.550700</td>\n",
       "      <td>37.577600</td>\n",
       "      <td>37.645000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lon</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>126.991397</td>\n",
       "      <td>0.079435</td>\n",
       "      <td>126.826000</td>\n",
       "      <td>126.937000</td>\n",
       "      <td>126.995000</td>\n",
       "      <td>127.042000</td>\n",
       "      <td>127.135000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEM</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>61.867972</td>\n",
       "      <td>54.279780</td>\n",
       "      <td>12.370000</td>\n",
       "      <td>28.700000</td>\n",
       "      <td>45.716000</td>\n",
       "      <td>59.832400</td>\n",
       "      <td>212.335000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slope</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>1.257048</td>\n",
       "      <td>1.370444</td>\n",
       "      <td>0.098475</td>\n",
       "      <td>0.271300</td>\n",
       "      <td>0.618000</td>\n",
       "      <td>1.767800</td>\n",
       "      <td>5.178230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Solar radiation</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>5341.502803</td>\n",
       "      <td>429.158867</td>\n",
       "      <td>4329.520508</td>\n",
       "      <td>4999.018555</td>\n",
       "      <td>5436.345215</td>\n",
       "      <td>5728.316406</td>\n",
       "      <td>5992.895996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Next_Tmax</th>\n",
       "      <td>7725.0</td>\n",
       "      <td>30.274887</td>\n",
       "      <td>3.128010</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>28.200000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.600000</td>\n",
       "      <td>38.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Next_Tmin</th>\n",
       "      <td>7725.0</td>\n",
       "      <td>22.932220</td>\n",
       "      <td>2.487613</td>\n",
       "      <td>11.300000</td>\n",
       "      <td>21.300000</td>\n",
       "      <td>23.100000</td>\n",
       "      <td>24.600000</td>\n",
       "      <td>29.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count         mean         std          min          25%  \\\n",
       "station           7750.0    13.000000    7.211568     1.000000     7.000000   \n",
       "Present_Tmax      7682.0    29.768211    2.969999    20.000000    27.800000   \n",
       "Present_Tmin      7682.0    23.225059    2.413961    11.300000    21.700000   \n",
       "LDAPS_RHmin       7677.0    56.759372   14.668111    19.794666    45.963543   \n",
       "LDAPS_RHmax       7677.0    88.374804    7.192004    58.936283    84.222862   \n",
       "LDAPS_Tmax_lapse  7677.0    29.613447    2.947191    17.624954    27.673499   \n",
       "LDAPS_Tmin_lapse  7677.0    23.512589    2.345347    14.272646    22.089739   \n",
       "LDAPS_WS          7677.0     7.097875    2.183836     2.882580     5.678705   \n",
       "LDAPS_LH          7677.0    62.505019   33.730589   -13.603212    37.266753   \n",
       "LDAPS_CC1         7677.0     0.368774    0.262458     0.000000     0.146654   \n",
       "LDAPS_CC2         7677.0     0.356080    0.258061     0.000000     0.140615   \n",
       "LDAPS_CC3         7677.0     0.318404    0.250362     0.000000     0.101388   \n",
       "LDAPS_CC4         7677.0     0.299191    0.254348     0.000000     0.081532   \n",
       "LDAPS_PPT1        7677.0     0.591995    1.945768     0.000000     0.000000   \n",
       "LDAPS_PPT2        7677.0     0.485003    1.762807     0.000000     0.000000   \n",
       "LDAPS_PPT3        7677.0     0.278200    1.161809     0.000000     0.000000   \n",
       "LDAPS_PPT4        7677.0     0.269407    1.206214     0.000000     0.000000   \n",
       "lat               7752.0    37.544722    0.050352    37.456200    37.510200   \n",
       "lon               7752.0   126.991397    0.079435   126.826000   126.937000   \n",
       "DEM               7752.0    61.867972   54.279780    12.370000    28.700000   \n",
       "Slope             7752.0     1.257048    1.370444     0.098475     0.271300   \n",
       "Solar radiation   7752.0  5341.502803  429.158867  4329.520508  4999.018555   \n",
       "Next_Tmax         7725.0    30.274887    3.128010    17.400000    28.200000   \n",
       "Next_Tmin         7725.0    22.932220    2.487613    11.300000    21.300000   \n",
       "\n",
       "                          50%          75%          max  \n",
       "station             13.000000    19.000000    25.000000  \n",
       "Present_Tmax        29.900000    32.000000    37.600000  \n",
       "Present_Tmin        23.400000    24.900000    29.900000  \n",
       "LDAPS_RHmin         55.039024    67.190056    98.524734  \n",
       "LDAPS_RHmax         89.793480    93.743629   100.000153  \n",
       "LDAPS_Tmax_lapse    29.703426    31.710450    38.542255  \n",
       "LDAPS_Tmin_lapse    23.760199    25.152909    29.619342  \n",
       "LDAPS_WS             6.547470     8.032276    21.857621  \n",
       "LDAPS_LH            56.865482    84.223616   213.414006  \n",
       "LDAPS_CC1            0.315697     0.575489     0.967277  \n",
       "LDAPS_CC2            0.312421     0.558694     0.968353  \n",
       "LDAPS_CC3            0.262555     0.496703     0.983789  \n",
       "LDAPS_CC4            0.227664     0.499489     0.974710  \n",
       "LDAPS_PPT1           0.000000     0.052525    23.701544  \n",
       "LDAPS_PPT2           0.000000     0.018364    21.621661  \n",
       "LDAPS_PPT3           0.000000     0.007896    15.841235  \n",
       "LDAPS_PPT4           0.000000     0.000041    16.655469  \n",
       "lat                 37.550700    37.577600    37.645000  \n",
       "lon                126.995000   127.042000   127.135000  \n",
       "DEM                 45.716000    59.832400   212.335000  \n",
       "Slope                0.618000     1.767800     5.178230  \n",
       "Solar radiation   5436.345215  5728.316406  5992.895996  \n",
       "Next_Tmax           30.500000    32.600000    38.900000  \n",
       "Next_Tmin           23.100000    24.600000    29.800000  "
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe().T "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:12:45.952260Z",
     "start_time": "2020-06-16T00:12:45.895274Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7752 entries, 0 to 7751\n",
      "Data columns (total 25 columns):\n",
      " #   Column            Non-Null Count  Dtype         \n",
      "---  ------            --------------  -----         \n",
      " 0   station           7750 non-null   float64       \n",
      " 1   Date              7750 non-null   datetime64[ns]\n",
      " 2   Present_Tmax      7682 non-null   float64       \n",
      " 3   Present_Tmin      7682 non-null   float64       \n",
      " 4   LDAPS_RHmin       7677 non-null   float64       \n",
      " 5   LDAPS_RHmax       7677 non-null   float64       \n",
      " 6   LDAPS_Tmax_lapse  7677 non-null   float64       \n",
      " 7   LDAPS_Tmin_lapse  7677 non-null   float64       \n",
      " 8   LDAPS_WS          7677 non-null   float64       \n",
      " 9   LDAPS_LH          7677 non-null   float64       \n",
      " 10  LDAPS_CC1         7677 non-null   float64       \n",
      " 11  LDAPS_CC2         7677 non-null   float64       \n",
      " 12  LDAPS_CC3         7677 non-null   float64       \n",
      " 13  LDAPS_CC4         7677 non-null   float64       \n",
      " 14  LDAPS_PPT1        7677 non-null   float64       \n",
      " 15  LDAPS_PPT2        7677 non-null   float64       \n",
      " 16  LDAPS_PPT3        7677 non-null   float64       \n",
      " 17  LDAPS_PPT4        7677 non-null   float64       \n",
      " 18  lat               7752 non-null   float64       \n",
      " 19  lon               7752 non-null   float64       \n",
      " 20  DEM               7752 non-null   float64       \n",
      " 21  Slope             7752 non-null   float64       \n",
      " 22  Solar radiation   7752 non-null   float64       \n",
      " 23  Next_Tmax         7725 non-null   float64       \n",
      " 24  Next_Tmin         7725 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(24)\n",
      "memory usage: 1.5 MB\n"
     ]
    }
   ],
   "source": [
    "data.info() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 查看空值（5分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:12:46.101859Z",
     "start_time": "2020-06-16T00:12:45.955239Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "station              2\n",
       "Date                 2\n",
       "Present_Tmax        70\n",
       "Present_Tmin        70\n",
       "LDAPS_RHmin         75\n",
       "LDAPS_RHmax         75\n",
       "LDAPS_Tmax_lapse    75\n",
       "LDAPS_Tmin_lapse    75\n",
       "LDAPS_WS            75\n",
       "LDAPS_LH            75\n",
       "LDAPS_CC1           75\n",
       "LDAPS_CC2           75\n",
       "LDAPS_CC3           75\n",
       "LDAPS_CC4           75\n",
       "LDAPS_PPT1          75\n",
       "LDAPS_PPT2          75\n",
       "LDAPS_PPT3          75\n",
       "LDAPS_PPT4          75\n",
       "lat                  0\n",
       "lon                  0\n",
       "DEM                  0\n",
       "Slope                0\n",
       "Solar radiation      0\n",
       "Next_Tmax           27\n",
       "Next_Tmin           27\n",
       "dtype: int64"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:12:46.301511Z",
     "start_time": "2020-06-16T00:12:46.110848Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station</th>\n",
       "      <th>Date</th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>Present_Tmin</th>\n",
       "      <th>LDAPS_RHmin</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_Tmin_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>...</th>\n",
       "      <th>LDAPS_PPT2</th>\n",
       "      <th>LDAPS_PPT3</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "      <th>Next_Tmax</th>\n",
       "      <th>Next_Tmin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-07-09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.051193</td>\n",
       "      <td>99.668961</td>\n",
       "      <td>27.872808</td>\n",
       "      <td>22.907420</td>\n",
       "      <td>11.017837</td>\n",
       "      <td>44.002020</td>\n",
       "      <td>...</td>\n",
       "      <td>0.036680</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.6046</td>\n",
       "      <td>126.991</td>\n",
       "      <td>212.3350</td>\n",
       "      <td>2.785000</td>\n",
       "      <td>5925.883789</td>\n",
       "      <td>23.4</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>22.0</td>\n",
       "      <td>2013-07-10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.196007</td>\n",
       "      <td>95.168205</td>\n",
       "      <td>28.097980</td>\n",
       "      <td>24.510159</td>\n",
       "      <td>8.374849</td>\n",
       "      <td>38.782242</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007261</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.5102</td>\n",
       "      <td>127.086</td>\n",
       "      <td>21.9668</td>\n",
       "      <td>0.133200</td>\n",
       "      <td>5772.487305</td>\n",
       "      <td>26.1</td>\n",
       "      <td>24.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-07-12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>95.027298</td>\n",
       "      <td>99.209839</td>\n",
       "      <td>24.078120</td>\n",
       "      <td>21.866817</td>\n",
       "      <td>8.543768</td>\n",
       "      <td>9.371270</td>\n",
       "      <td>...</td>\n",
       "      <td>5.055660</td>\n",
       "      <td>1.347418</td>\n",
       "      <td>0.980052</td>\n",
       "      <td>37.6046</td>\n",
       "      <td>126.991</td>\n",
       "      <td>212.3350</td>\n",
       "      <td>2.785000</td>\n",
       "      <td>5893.265625</td>\n",
       "      <td>23.2</td>\n",
       "      <td>20.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-07-18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60.891193</td>\n",
       "      <td>94.747780</td>\n",
       "      <td>29.195536</td>\n",
       "      <td>23.236973</td>\n",
       "      <td>10.881031</td>\n",
       "      <td>79.349271</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.057358</td>\n",
       "      <td>37.6046</td>\n",
       "      <td>126.991</td>\n",
       "      <td>212.3350</td>\n",
       "      <td>2.785000</td>\n",
       "      <td>5812.293457</td>\n",
       "      <td>27.6</td>\n",
       "      <td>21.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464</th>\n",
       "      <td>15.0</td>\n",
       "      <td>2013-07-18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52.795406</td>\n",
       "      <td>83.902847</td>\n",
       "      <td>31.480089</td>\n",
       "      <td>25.607262</td>\n",
       "      <td>8.995135</td>\n",
       "      <td>26.022306</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008702</td>\n",
       "      <td>37.5507</td>\n",
       "      <td>126.937</td>\n",
       "      <td>30.0464</td>\n",
       "      <td>0.855200</td>\n",
       "      <td>5681.875000</td>\n",
       "      <td>30.7</td>\n",
       "      <td>23.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7629</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2017-08-26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.755058</td>\n",
       "      <td>83.340240</td>\n",
       "      <td>25.842338</td>\n",
       "      <td>18.532986</td>\n",
       "      <td>4.926595</td>\n",
       "      <td>97.230757</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.5507</td>\n",
       "      <td>127.135</td>\n",
       "      <td>35.0380</td>\n",
       "      <td>0.505500</td>\n",
       "      <td>4602.118164</td>\n",
       "      <td>26.1</td>\n",
       "      <td>17.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7682</th>\n",
       "      <td>8.0</td>\n",
       "      <td>2017-08-28</td>\n",
       "      <td>26.3</td>\n",
       "      <td>18.1</td>\n",
       "      <td>29.959215</td>\n",
       "      <td>90.116638</td>\n",
       "      <td>23.135079</td>\n",
       "      <td>17.282587</td>\n",
       "      <td>9.292264</td>\n",
       "      <td>75.430868</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.4697</td>\n",
       "      <td>126.910</td>\n",
       "      <td>52.5180</td>\n",
       "      <td>1.562900</td>\n",
       "      <td>4518.488770</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7707</th>\n",
       "      <td>8.0</td>\n",
       "      <td>2017-08-29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.392651</td>\n",
       "      <td>75.728195</td>\n",
       "      <td>22.223247</td>\n",
       "      <td>15.954970</td>\n",
       "      <td>4.764492</td>\n",
       "      <td>37.786237</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.4697</td>\n",
       "      <td>126.910</td>\n",
       "      <td>52.5180</td>\n",
       "      <td>1.562900</td>\n",
       "      <td>4478.937012</td>\n",
       "      <td>23.1</td>\n",
       "      <td>16.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7750</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>20.0</td>\n",
       "      <td>11.3</td>\n",
       "      <td>19.794666</td>\n",
       "      <td>58.936283</td>\n",
       "      <td>17.624954</td>\n",
       "      <td>14.272646</td>\n",
       "      <td>2.882580</td>\n",
       "      <td>-13.603212</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.4562</td>\n",
       "      <td>126.826</td>\n",
       "      <td>12.3700</td>\n",
       "      <td>0.098475</td>\n",
       "      <td>4329.520508</td>\n",
       "      <td>17.4</td>\n",
       "      <td>11.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7751</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>37.6</td>\n",
       "      <td>29.9</td>\n",
       "      <td>98.524734</td>\n",
       "      <td>100.000153</td>\n",
       "      <td>38.542255</td>\n",
       "      <td>29.619342</td>\n",
       "      <td>21.857621</td>\n",
       "      <td>213.414006</td>\n",
       "      <td>...</td>\n",
       "      <td>21.621661</td>\n",
       "      <td>15.841235</td>\n",
       "      <td>16.655469</td>\n",
       "      <td>37.6450</td>\n",
       "      <td>127.135</td>\n",
       "      <td>212.3350</td>\n",
       "      <td>5.178230</td>\n",
       "      <td>5992.895996</td>\n",
       "      <td>38.9</td>\n",
       "      <td>29.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>164 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      station       Date  Present_Tmax  Present_Tmin  LDAPS_RHmin  \\\n",
       "225       1.0 2013-07-09           NaN           NaN    70.051193   \n",
       "271      22.0 2013-07-10           NaN           NaN    72.196007   \n",
       "300       1.0 2013-07-12           NaN           NaN    95.027298   \n",
       "450       1.0 2013-07-18           NaN           NaN    60.891193   \n",
       "464      15.0 2013-07-18           NaN           NaN    52.795406   \n",
       "...       ...        ...           ...           ...          ...   \n",
       "7629      5.0 2017-08-26           NaN           NaN    43.755058   \n",
       "7682      8.0 2017-08-28          26.3          18.1    29.959215   \n",
       "7707      8.0 2017-08-29           NaN           NaN    44.392651   \n",
       "7750      NaN        NaT          20.0          11.3    19.794666   \n",
       "7751      NaN        NaT          37.6          29.9    98.524734   \n",
       "\n",
       "      LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_Tmin_lapse   LDAPS_WS    LDAPS_LH  \\\n",
       "225     99.668961         27.872808         22.907420  11.017837   44.002020   \n",
       "271     95.168205         28.097980         24.510159   8.374849   38.782242   \n",
       "300     99.209839         24.078120         21.866817   8.543768    9.371270   \n",
       "450     94.747780         29.195536         23.236973  10.881031   79.349271   \n",
       "464     83.902847         31.480089         25.607262   8.995135   26.022306   \n",
       "...           ...               ...               ...        ...         ...   \n",
       "7629    83.340240         25.842338         18.532986   4.926595   97.230757   \n",
       "7682    90.116638         23.135079         17.282587   9.292264   75.430868   \n",
       "7707    75.728195         22.223247         15.954970   4.764492   37.786237   \n",
       "7750    58.936283         17.624954         14.272646   2.882580  -13.603212   \n",
       "7751   100.000153         38.542255         29.619342  21.857621  213.414006   \n",
       "\n",
       "      ...  LDAPS_PPT2  LDAPS_PPT3  LDAPS_PPT4      lat      lon       DEM  \\\n",
       "225   ...    0.036680    0.000000    0.000000  37.6046  126.991  212.3350   \n",
       "271   ...    0.007261    0.000000    0.000000  37.5102  127.086   21.9668   \n",
       "300   ...    5.055660    1.347418    0.980052  37.6046  126.991  212.3350   \n",
       "450   ...    0.000000    0.000000    0.057358  37.6046  126.991  212.3350   \n",
       "464   ...    0.000000    0.000000    0.008702  37.5507  126.937   30.0464   \n",
       "...   ...         ...         ...         ...      ...      ...       ...   \n",
       "7629  ...    0.000000    0.000000    0.000000  37.5507  127.135   35.0380   \n",
       "7682  ...    0.000000    0.000000    0.000000  37.4697  126.910   52.5180   \n",
       "7707  ...    0.000000    0.000000    0.000000  37.4697  126.910   52.5180   \n",
       "7750  ...    0.000000    0.000000    0.000000  37.4562  126.826   12.3700   \n",
       "7751  ...   21.621661   15.841235   16.655469  37.6450  127.135  212.3350   \n",
       "\n",
       "         Slope  Solar radiation  Next_Tmax  Next_Tmin  \n",
       "225   2.785000      5925.883789       23.4       22.0  \n",
       "271   0.133200      5772.487305       26.1       24.1  \n",
       "300   2.785000      5893.265625       23.2       20.5  \n",
       "450   2.785000      5812.293457       27.6       21.8  \n",
       "464   0.855200      5681.875000       30.7       23.4  \n",
       "...        ...              ...        ...        ...  \n",
       "7629  0.505500      4602.118164       26.1       17.9  \n",
       "7682  1.562900      4518.488770        NaN        NaN  \n",
       "7707  1.562900      4478.937012       23.1       16.3  \n",
       "7750  0.098475      4329.520508       17.4       11.3  \n",
       "7751  5.178230      5992.895996       38.9       29.8  \n",
       "\n",
       "[164 rows x 25 columns]"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 取出所有有空值的记录  （5分）\n",
    "data_empty = data.apply(lambda x:x.isna().sum() > 0,axis=1 )\n",
    "data_null = data.loc[data_empty,:]\n",
    "data_null"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 查看每列数据的分布状态"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:12:55.448321Z",
     "start_time": "2020-06-16T00:12:46.308487Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'station'}>,\n",
       "        <AxesSubplot:title={'center':'Present_Tmax'}>,\n",
       "        <AxesSubplot:title={'center':'Present_Tmin'}>,\n",
       "        <AxesSubplot:title={'center':'LDAPS_RHmin'}>],\n",
       "       [<AxesSubplot:title={'center':'LDAPS_RHmax'}>,\n",
       "        <AxesSubplot:title={'center':'LDAPS_Tmax_lapse'}>,\n",
       "        <AxesSubplot:title={'center':'LDAPS_Tmin_lapse'}>,\n",
       "        <AxesSubplot:title={'center':'LDAPS_WS'}>],\n",
       "       [<AxesSubplot:title={'center':'LDAPS_LH'}>,\n",
       "        <AxesSubplot:title={'center':'LDAPS_CC1'}>,\n",
       "        <AxesSubplot:title={'center':'LDAPS_CC2'}>,\n",
       "        <AxesSubplot:title={'center':'LDAPS_CC3'}>],\n",
       "       [<AxesSubplot:title={'center':'LDAPS_CC4'}>,\n",
       "        <AxesSubplot:title={'center':'LDAPS_PPT1'}>,\n",
       "        <AxesSubplot:title={'center':'LDAPS_PPT2'}>,\n",
       "        <AxesSubplot:title={'center':'LDAPS_PPT3'}>],\n",
       "       [<AxesSubplot:title={'center':'LDAPS_PPT4'}>,\n",
       "        <AxesSubplot:title={'center':'lat'}>,\n",
       "        <AxesSubplot:title={'center':'lon'}>,\n",
       "        <AxesSubplot:title={'center':'DEM'}>],\n",
       "       [<AxesSubplot:title={'center':'Slope'}>,\n",
       "        <AxesSubplot:title={'center':'Solar radiation'}>,\n",
       "        <AxesSubplot:title={'center':'Next_Tmax'}>,\n",
       "        <AxesSubplot:title={'center':'Next_Tmin'}>]], dtype=object)"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 24 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制每列数据的直方图（分布频次）\n",
    "plot_map = data.drop('Date',axis=1) # 删除'Date'列\n",
    "\n",
    "# density=True 表示柱子的高度为每个区间的频率\n",
    "# grid表示是否要显示网格线\n",
    "%matplotlib inline\n",
    "plot_map.hist(density = True,grid = False,layout = (6,4),figsize = (15,15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:21.940528Z",
     "start_time": "2020-06-16T00:12:55.451319Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1a5ecdc0cec846ab9430dfae5c3a2392",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9a0a01d662d04ea190e09af3ae8d3d3a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "677fd6526f57446ca65aab032710b920",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dd731e71a7404e5d9b2ba716623e0cd3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Export report to file:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 生成整体数据概要\n",
    "# data_report=pp.ProfileReport(data) \n",
    "# data_report\n",
    "\n",
    "# 也可以把生成的报告导出保存\n",
    "data_report.to_file('report.html') \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据预处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 提取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:33:52.629282Z",
     "start_time": "2020-06-16T01:33:52.621285Z"
    }
   },
   "outputs": [],
   "source": [
    "# 由于本数据集的特殊性（有两个样本标签：次日最高气温和最低气温）\n",
    "#，本案例中我们只进行次日最高温预测--因此这里只选出与最高温预测相关的变量，作为建模要用的数据\n",
    "\n",
    "Max = data[['Present_Tmax','LDAPS_RHmax','LDAPS_Tmax_lapse','LDAPS_WS','LDAPS_LH','LDAPS_CC1','LDAPS_CC2','LDAPS_CC3','LDAPS_CC4','LDAPS_PPT1','LDAPS_PPT4',\n",
    "          'lat','lon','DEM','Slope','Solar radiation','Next_Tmax']] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:33:56.575804Z",
     "start_time": "2020-06-16T01:33:56.551818Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "      <th>Next_Tmax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28.7</td>\n",
       "      <td>91.116364</td>\n",
       "      <td>28.074101</td>\n",
       "      <td>6.818887</td>\n",
       "      <td>69.451805</td>\n",
       "      <td>0.233947</td>\n",
       "      <td>0.203896</td>\n",
       "      <td>0.161697</td>\n",
       "      <td>0.130928</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6046</td>\n",
       "      <td>126.991</td>\n",
       "      <td>212.3350</td>\n",
       "      <td>2.7850</td>\n",
       "      <td>5992.895996</td>\n",
       "      <td>29.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31.9</td>\n",
       "      <td>90.604721</td>\n",
       "      <td>29.850689</td>\n",
       "      <td>5.691890</td>\n",
       "      <td>51.937448</td>\n",
       "      <td>0.225508</td>\n",
       "      <td>0.251771</td>\n",
       "      <td>0.159444</td>\n",
       "      <td>0.127727</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6046</td>\n",
       "      <td>127.032</td>\n",
       "      <td>44.7624</td>\n",
       "      <td>0.5141</td>\n",
       "      <td>5869.312500</td>\n",
       "      <td>30.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31.6</td>\n",
       "      <td>83.973587</td>\n",
       "      <td>30.091292</td>\n",
       "      <td>6.138224</td>\n",
       "      <td>20.573050</td>\n",
       "      <td>0.209344</td>\n",
       "      <td>0.257469</td>\n",
       "      <td>0.204091</td>\n",
       "      <td>0.142125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.5776</td>\n",
       "      <td>127.058</td>\n",
       "      <td>33.3068</td>\n",
       "      <td>0.2661</td>\n",
       "      <td>5863.555664</td>\n",
       "      <td>31.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32.0</td>\n",
       "      <td>96.483688</td>\n",
       "      <td>29.704629</td>\n",
       "      <td>5.650050</td>\n",
       "      <td>65.727144</td>\n",
       "      <td>0.216372</td>\n",
       "      <td>0.226002</td>\n",
       "      <td>0.161157</td>\n",
       "      <td>0.134249</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6450</td>\n",
       "      <td>127.022</td>\n",
       "      <td>45.7160</td>\n",
       "      <td>2.5348</td>\n",
       "      <td>5856.964844</td>\n",
       "      <td>31.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31.4</td>\n",
       "      <td>90.155128</td>\n",
       "      <td>29.113934</td>\n",
       "      <td>5.735004</td>\n",
       "      <td>107.965535</td>\n",
       "      <td>0.151407</td>\n",
       "      <td>0.249995</td>\n",
       "      <td>0.178892</td>\n",
       "      <td>0.170021</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.5507</td>\n",
       "      <td>127.135</td>\n",
       "      <td>35.0380</td>\n",
       "      <td>0.5055</td>\n",
       "      <td>5859.552246</td>\n",
       "      <td>31.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS    LDAPS_LH  \\\n",
       "0          28.7    91.116364         28.074101  6.818887   69.451805   \n",
       "1          31.9    90.604721         29.850689  5.691890   51.937448   \n",
       "2          31.6    83.973587         30.091292  6.138224   20.573050   \n",
       "3          32.0    96.483688         29.704629  5.650050   65.727144   \n",
       "4          31.4    90.155128         29.113934  5.735004  107.965535   \n",
       "\n",
       "   LDAPS_CC1  LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4  \\\n",
       "0   0.233947   0.203896   0.161697   0.130928         0.0         0.0   \n",
       "1   0.225508   0.251771   0.159444   0.127727         0.0         0.0   \n",
       "2   0.209344   0.257469   0.204091   0.142125         0.0         0.0   \n",
       "3   0.216372   0.226002   0.161157   0.134249         0.0         0.0   \n",
       "4   0.151407   0.249995   0.178892   0.170021         0.0         0.0   \n",
       "\n",
       "       lat      lon       DEM   Slope  Solar radiation  Next_Tmax  \n",
       "0  37.6046  126.991  212.3350  2.7850      5992.895996       29.1  \n",
       "1  37.6046  127.032   44.7624  0.5141      5869.312500       30.5  \n",
       "2  37.5776  127.058   33.3068  0.2661      5863.555664       31.1  \n",
       "3  37.6450  127.022   45.7160  2.5348      5856.964844       31.7  \n",
       "4  37.5507  127.135   35.0380  0.5055      5859.552246       31.2  "
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Max.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:22.290321Z",
     "start_time": "2020-06-16T00:15:22.130413Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7752 entries, 0 to 7751\n",
      "Data columns (total 17 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Present_Tmax      7682 non-null   float64\n",
      " 1   LDAPS_RHmax       7677 non-null   float64\n",
      " 2   LDAPS_Tmax_lapse  7677 non-null   float64\n",
      " 3   LDAPS_WS          7677 non-null   float64\n",
      " 4   LDAPS_LH          7677 non-null   float64\n",
      " 5   LDAPS_CC1         7677 non-null   float64\n",
      " 6   LDAPS_CC2         7677 non-null   float64\n",
      " 7   LDAPS_CC3         7677 non-null   float64\n",
      " 8   LDAPS_CC4         7677 non-null   float64\n",
      " 9   LDAPS_PPT1        7677 non-null   float64\n",
      " 10  LDAPS_PPT4        7677 non-null   float64\n",
      " 11  lat               7752 non-null   float64\n",
      " 12  lon               7752 non-null   float64\n",
      " 13  DEM               7752 non-null   float64\n",
      " 14  Slope             7752 non-null   float64\n",
      " 15  Solar radiation   7752 non-null   float64\n",
      " 16  Next_Tmax         7725 non-null   float64\n",
      "dtypes: float64(17)\n",
      "memory usage: 1.0 MB\n"
     ]
    }
   ],
   "source": [
    "Max.info() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:22.570160Z",
     "start_time": "2020-06-16T00:15:22.293320Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Present_Tmax</th>\n",
       "      <td>7682.0</td>\n",
       "      <td>29.768211</td>\n",
       "      <td>2.969999</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>27.800000</td>\n",
       "      <td>29.900000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>37.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>88.374804</td>\n",
       "      <td>7.192004</td>\n",
       "      <td>58.936283</td>\n",
       "      <td>84.222862</td>\n",
       "      <td>89.793480</td>\n",
       "      <td>93.743629</td>\n",
       "      <td>100.000153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>29.613447</td>\n",
       "      <td>2.947191</td>\n",
       "      <td>17.624954</td>\n",
       "      <td>27.673499</td>\n",
       "      <td>29.703426</td>\n",
       "      <td>31.710450</td>\n",
       "      <td>38.542255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>7.097875</td>\n",
       "      <td>2.183836</td>\n",
       "      <td>2.882580</td>\n",
       "      <td>5.678705</td>\n",
       "      <td>6.547470</td>\n",
       "      <td>8.032276</td>\n",
       "      <td>21.857621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>62.505019</td>\n",
       "      <td>33.730589</td>\n",
       "      <td>-13.603212</td>\n",
       "      <td>37.266753</td>\n",
       "      <td>56.865482</td>\n",
       "      <td>84.223616</td>\n",
       "      <td>213.414006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.368774</td>\n",
       "      <td>0.262458</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.146654</td>\n",
       "      <td>0.315697</td>\n",
       "      <td>0.575489</td>\n",
       "      <td>0.967277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.356080</td>\n",
       "      <td>0.258061</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.140615</td>\n",
       "      <td>0.312421</td>\n",
       "      <td>0.558694</td>\n",
       "      <td>0.968353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.318404</td>\n",
       "      <td>0.250362</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.101388</td>\n",
       "      <td>0.262555</td>\n",
       "      <td>0.496703</td>\n",
       "      <td>0.983789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.299191</td>\n",
       "      <td>0.254348</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.081532</td>\n",
       "      <td>0.227664</td>\n",
       "      <td>0.499489</td>\n",
       "      <td>0.974710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.591995</td>\n",
       "      <td>1.945768</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.052525</td>\n",
       "      <td>23.701544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.269407</td>\n",
       "      <td>1.206214</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>16.655469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lat</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>37.544722</td>\n",
       "      <td>0.050352</td>\n",
       "      <td>37.456200</td>\n",
       "      <td>37.510200</td>\n",
       "      <td>37.550700</td>\n",
       "      <td>37.577600</td>\n",
       "      <td>37.645000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lon</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>126.991397</td>\n",
       "      <td>0.079435</td>\n",
       "      <td>126.826000</td>\n",
       "      <td>126.937000</td>\n",
       "      <td>126.995000</td>\n",
       "      <td>127.042000</td>\n",
       "      <td>127.135000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEM</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>61.867972</td>\n",
       "      <td>54.279780</td>\n",
       "      <td>12.370000</td>\n",
       "      <td>28.700000</td>\n",
       "      <td>45.716000</td>\n",
       "      <td>59.832400</td>\n",
       "      <td>212.335000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slope</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>1.257048</td>\n",
       "      <td>1.370444</td>\n",
       "      <td>0.098475</td>\n",
       "      <td>0.271300</td>\n",
       "      <td>0.618000</td>\n",
       "      <td>1.767800</td>\n",
       "      <td>5.178230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Solar radiation</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>5341.502803</td>\n",
       "      <td>429.158867</td>\n",
       "      <td>4329.520508</td>\n",
       "      <td>4999.018555</td>\n",
       "      <td>5436.345215</td>\n",
       "      <td>5728.316406</td>\n",
       "      <td>5992.895996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Next_Tmax</th>\n",
       "      <td>7725.0</td>\n",
       "      <td>30.274887</td>\n",
       "      <td>3.128010</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>28.200000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.600000</td>\n",
       "      <td>38.900000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count         mean         std          min          25%  \\\n",
       "Present_Tmax      7682.0    29.768211    2.969999    20.000000    27.800000   \n",
       "LDAPS_RHmax       7677.0    88.374804    7.192004    58.936283    84.222862   \n",
       "LDAPS_Tmax_lapse  7677.0    29.613447    2.947191    17.624954    27.673499   \n",
       "LDAPS_WS          7677.0     7.097875    2.183836     2.882580     5.678705   \n",
       "LDAPS_LH          7677.0    62.505019   33.730589   -13.603212    37.266753   \n",
       "LDAPS_CC1         7677.0     0.368774    0.262458     0.000000     0.146654   \n",
       "LDAPS_CC2         7677.0     0.356080    0.258061     0.000000     0.140615   \n",
       "LDAPS_CC3         7677.0     0.318404    0.250362     0.000000     0.101388   \n",
       "LDAPS_CC4         7677.0     0.299191    0.254348     0.000000     0.081532   \n",
       "LDAPS_PPT1        7677.0     0.591995    1.945768     0.000000     0.000000   \n",
       "LDAPS_PPT4        7677.0     0.269407    1.206214     0.000000     0.000000   \n",
       "lat               7752.0    37.544722    0.050352    37.456200    37.510200   \n",
       "lon               7752.0   126.991397    0.079435   126.826000   126.937000   \n",
       "DEM               7752.0    61.867972   54.279780    12.370000    28.700000   \n",
       "Slope             7752.0     1.257048    1.370444     0.098475     0.271300   \n",
       "Solar radiation   7752.0  5341.502803  429.158867  4329.520508  4999.018555   \n",
       "Next_Tmax         7725.0    30.274887    3.128010    17.400000    28.200000   \n",
       "\n",
       "                          50%          75%          max  \n",
       "Present_Tmax        29.900000    32.000000    37.600000  \n",
       "LDAPS_RHmax         89.793480    93.743629   100.000153  \n",
       "LDAPS_Tmax_lapse    29.703426    31.710450    38.542255  \n",
       "LDAPS_WS             6.547470     8.032276    21.857621  \n",
       "LDAPS_LH            56.865482    84.223616   213.414006  \n",
       "LDAPS_CC1            0.315697     0.575489     0.967277  \n",
       "LDAPS_CC2            0.312421     0.558694     0.968353  \n",
       "LDAPS_CC3            0.262555     0.496703     0.983789  \n",
       "LDAPS_CC4            0.227664     0.499489     0.974710  \n",
       "LDAPS_PPT1           0.000000     0.052525    23.701544  \n",
       "LDAPS_PPT4           0.000000     0.000041    16.655469  \n",
       "lat                 37.550700    37.577600    37.645000  \n",
       "lon                126.995000   127.042000   127.135000  \n",
       "DEM                 45.716000    59.832400   212.335000  \n",
       "Slope                0.618000     1.767800     5.178230  \n",
       "Solar radiation   5436.345215  5728.316406  5992.895996  \n",
       "Next_Tmax           30.500000    32.600000    38.900000  "
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Max.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 填充缺失值（10分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:34:07.620687Z",
     "start_time": "2020-06-16T01:34:07.603697Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 用每列的均值填充其缺失值\n",
    "for I in Max.columns:\n",
    "    Max[I].fillna(Max[I].mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Present_Tmax        0\n",
       "LDAPS_RHmax         0\n",
       "LDAPS_Tmax_lapse    0\n",
       "LDAPS_WS            0\n",
       "LDAPS_LH            0\n",
       "LDAPS_CC1           0\n",
       "LDAPS_CC2           0\n",
       "LDAPS_CC3           0\n",
       "LDAPS_CC4           0\n",
       "LDAPS_PPT1          0\n",
       "LDAPS_PPT4          0\n",
       "lat                 0\n",
       "lon                 0\n",
       "DEM                 0\n",
       "Slope               0\n",
       "Solar radiation     0\n",
       "Next_Tmax           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Max.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 标准化处理连续型特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T02:15:24.140777Z",
     "start_time": "2020-06-16T02:15:24.099800Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "      <th>Next_Tmax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.361326</td>\n",
       "      <td>0.383078</td>\n",
       "      <td>-0.524889</td>\n",
       "      <td>-0.128382</td>\n",
       "      <td>0.206966</td>\n",
       "      <td>-0.516243</td>\n",
       "      <td>-0.592636</td>\n",
       "      <td>-0.629013</td>\n",
       "      <td>-0.664815</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>1.517935</td>\n",
       "      <td>-0.376282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.721084</td>\n",
       "      <td>0.311586</td>\n",
       "      <td>0.080895</td>\n",
       "      <td>-0.646994</td>\n",
       "      <td>-0.314841</td>\n",
       "      <td>-0.548557</td>\n",
       "      <td>-0.406199</td>\n",
       "      <td>-0.638055</td>\n",
       "      <td>-0.677462</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>0.511177</td>\n",
       "      <td>-0.315157</td>\n",
       "      <td>-0.542158</td>\n",
       "      <td>1.229950</td>\n",
       "      <td>0.072097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.619608</td>\n",
       "      <td>-0.614982</td>\n",
       "      <td>0.162936</td>\n",
       "      <td>-0.441604</td>\n",
       "      <td>-1.249283</td>\n",
       "      <td>-0.610450</td>\n",
       "      <td>-0.384009</td>\n",
       "      <td>-0.458843</td>\n",
       "      <td>-0.620575</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>0.838510</td>\n",
       "      <td>-0.526218</td>\n",
       "      <td>-0.723133</td>\n",
       "      <td>1.216534</td>\n",
       "      <td>0.264260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.754909</td>\n",
       "      <td>1.133054</td>\n",
       "      <td>0.031092</td>\n",
       "      <td>-0.666247</td>\n",
       "      <td>0.095997</td>\n",
       "      <td>-0.583539</td>\n",
       "      <td>-0.506548</td>\n",
       "      <td>-0.631178</td>\n",
       "      <td>-0.651696</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>0.385280</td>\n",
       "      <td>-0.297588</td>\n",
       "      <td>0.932424</td>\n",
       "      <td>1.201176</td>\n",
       "      <td>0.456422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.551957</td>\n",
       "      <td>0.248765</td>\n",
       "      <td>-0.170325</td>\n",
       "      <td>-0.627154</td>\n",
       "      <td>1.354409</td>\n",
       "      <td>-0.832287</td>\n",
       "      <td>-0.413115</td>\n",
       "      <td>-0.559990</td>\n",
       "      <td>-0.510358</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>-0.494322</td>\n",
       "      <td>-0.548433</td>\n",
       "      <td>1.207205</td>\n",
       "      <td>0.296287</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  LDAPS_CC1  \\\n",
       "0     -0.361326     0.383078         -0.524889 -0.128382  0.206966  -0.516243   \n",
       "1      0.721084     0.311586          0.080895 -0.646994 -0.314841  -0.548557   \n",
       "2      0.619608    -0.614982          0.162936 -0.441604 -1.249283  -0.610450   \n",
       "3      0.754909     1.133054          0.031092 -0.666247  0.095997  -0.583539   \n",
       "4      0.551957     0.248765         -0.170325 -0.627154  1.354409  -0.832287   \n",
       "\n",
       "   LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4       lat  \\\n",
       "0  -0.592636  -0.629013  -0.664815    -0.30575   -0.224453  1.189286   \n",
       "1  -0.406199  -0.638055  -0.677462    -0.30575   -0.224453  1.189286   \n",
       "2  -0.384009  -0.458843  -0.620575    -0.30575   -0.224453  0.653021   \n",
       "3  -0.506548  -0.631178  -0.651696    -0.30575   -0.224453  1.991696   \n",
       "4  -0.413115  -0.559990  -0.510358    -0.30575   -0.224453  0.118743   \n",
       "\n",
       "        lon       DEM     Slope  Solar radiation  Next_Tmax  \n",
       "0 -0.005000  2.772243  1.115004         1.517935  -0.376282  \n",
       "1  0.511177 -0.315157 -0.542158         1.229950   0.072097  \n",
       "2  0.838510 -0.526218 -0.723133         1.216534   0.264260  \n",
       "3  0.385280 -0.297588  0.932424         1.201176   0.456422  \n",
       "4  1.807917 -0.494322 -0.548433         1.207205   0.296287  "
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler \n",
    "from sklearn.model_selection import train_test_split\n",
    "#标准化处理连续型特征 （10分)\n",
    "std = StandardScaler().fit(Max)\n",
    "Max_ = std.transform(Max)\n",
    "Max_ = pd.DataFrame(Max_,columns=['Present_Tmax','LDAPS_RHmax','LDAPS_Tmax_lapse','LDAPS_WS','LDAPS_LH','LDAPS_CC1','LDAPS_CC2','LDAPS_CC3','LDAPS_CC4','LDAPS_PPT1','LDAPS_PPT4',\n",
    "          'lat','lon','DEM','Slope','Solar radiation','Next_Tmax'])\n",
    "Max_.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 划分特征列和标签列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:34:50.260685Z",
     "start_time": "2020-06-16T01:34:50.234704Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "x = Max_.iloc[:,:-1]\n",
    "y = Max_.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 创建线性回归模型并评估"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 划分数据集（5分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:35:15.723572Z",
     "start_time": "2020-06-16T01:35:15.714578Z"
    }
   },
   "outputs": [],
   "source": [
    "xtrain,xtest,ytrain,ytest = train_test_split(x,y,test_size=0.3,random_state=420)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 建模（5分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:35:18.264310Z",
     "start_time": "2020-06-16T01:35:18.247275Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression,RidgeCV,LassoCV,SGDRegressor\n",
    "lr = LinearRegression().fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T06:32:40.607647Z",
     "start_time": "2020-06-15T06:32:40.603648Z"
    }
   },
   "source": [
    "## 评估（5分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:35:23.925342Z",
     "start_time": "2020-06-16T01:35:23.911350Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7539674802919668 0.7548561002578509\n"
     ]
    }
   ],
   "source": [
    "print(lr.score(xtrain,ytrain),lr.score(xtest,ytest))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 计算MSE与RMSE（10分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:22:24.025817Z",
     "start_time": "2020-06-16T01:22:24.016823Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.24473776268159947\n",
      "RMSE: 0.4947097762138924\n"
     ]
    }
   ],
   "source": [
    "# MSE均方误差  RMSE误差均方根,为MSE开方后的结果\n",
    "from sklearn.metrics import mean_squared_error\n",
    "y_test_pred = lr.predict(xtest)\n",
    "MSE = mean_squared_error(ytest,y_test_pred)\n",
    "RMSE = np.sqrt(mean_squared_error(ytest,y_test_pred))\n",
    "print('MSE:',MSE)\n",
    "print('RMSE:',RMSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型诊断"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 绘制线性拟合图（10分）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:24.480812Z",
     "start_time": "2020-06-16T00:15:24.344885Z"
    }
   },
   "source": [
    "'''\n",
    "从上可以看到RMSE(越小越好）的值0.5，我们通过可视化来看一下训练后的预测和真实值之间的差异：\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:22:30.193284Z",
     "start_time": "2020-06-16T01:22:29.785111Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x720 with 0 Axes>"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2c09b647908>]"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2c09b6943c8>]"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2c0951a45f8>"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画个折线图来看看模型拟合的好坏程度\n",
    "plt.figure(figsize=(20,10)) \n",
    "plt.plot(range(len(ytest)), ytest, 'r', label='ytest')\n",
    "plt.plot(range(len(ytest)), y_test_pred, 'b', label='y_test_pred')\n",
    "plt.legend() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:25.123437Z",
     "start_time": "2020-06-16T00:15:25.118441Z"
    }
   },
   "source": [
    "'''\n",
    "可以看到许多地方，红线会明显超出蓝线，说明我们的模型拟合度不够，再换个散点图来更直观地看一下：\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:22:33.969981Z",
     "start_time": "2020-06-16T01:22:33.602723Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x432 with 0 Axes>"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2c0ad0bc518>"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2c0ad48cf28>]"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'y-test')"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'y-test-pred')"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#使用散点图观测（10分）\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.scatter(ytest,y_test_pred)\n",
    "plt.plot([ytest.min(),ytest.max()],[ytest.min(),ytest.max()],ls='--',c='black')\n",
    "plt.xlabel('y-test')\n",
    "plt.ylabel('y-test-pred')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:25.593168Z",
     "start_time": "2020-06-16T00:15:25.588171Z"
    }
   },
   "source": [
    "'''\n",
    "图中我们可以看到，如果完全拟合，散点应该和直线相重合，这里发现，y_test有一些的异常值，\n",
    "而线性回归模型的一大缺点就是对异常值很敏感，会极大影响模型的准确性，因此，下一步，我们就根据这一点，对模型进行优化\n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 绘制残差图（10分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:26.237802Z",
     "start_time": "2020-06-16T00:15:25.595167Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1008x1008 with 0 Axes>"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2c0a5aaf9b0>"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x2c0a58642b0>"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'y_observed')"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'y_predicted')"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制残差图查看模型的拟合情况（10分）\n",
    "from sklearn.model_selection import cross_val_predict,cross_val_score\n",
    "y_cross_pred = cross_val_predict(lr,x,y,cv=10)\n",
    "plt.figure(figsize=(14,14))\n",
    "plt.scatter(y,y-y_cross_pred) #y-y_cross_pred做残差\n",
    "plt.axhline(lw=7,c='b')\n",
    "plt.xlabel('y_observed')\n",
    "plt.ylabel('y_predicted')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 绘制相关性热图（10分）\n",
    "\n",
    "**绘制各预测变量间的相关性热图，查看是否存在相关性很高的变量（共线性或近似共线性）**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:27.379205Z",
     "start_time": "2020-06-16T00:15:26.240804Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x1080 with 0 Axes>"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Max_corr = Max_.corr()\n",
    "mask = np.array(Max_corr)\n",
    "mask[np.tril_indices_from(mask)] = False   #np.tril_indices_from()返回数组里下三角的索引值\n",
    "plt.figure(figsize=(15,15))\n",
    "sns.heatmap(Max_corr,mask=mask,vmax=1,square=True,annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型调优"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 删除异常值（10分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:35:34.870734Z",
     "start_time": "2020-06-16T01:35:34.674966Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 576x576 with 0 Axes>"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#####================寻找异常值====================######\n",
    "\n",
    "plt.figure(figsize=(8,8))\n",
    "Max_['Next_Tmax'].plot.box() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T02:28:18.424085Z",
     "start_time": "2020-06-16T02:28:18.411090Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.662770694023558"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "-3.8575878207889565"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 删除掉Next_Tmax中小于下边缘值的记录（10分）\n",
    "Q1,Q3 = np.percentile(Max_['Next_Tmax'],(0.25,0.75))\n",
    "IQR = Q3 - Q1\n",
    "up_edge = Q3 + 1.5*IQR\n",
    "low_edge = Q1 - 1.5*IQR\n",
    "up_edge\n",
    "low_edge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T02:16:00.616309Z",
     "start_time": "2020-06-16T02:16:00.579332Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "      <th>Next_Tmax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6175</th>\n",
       "      <td>-2.695272</td>\n",
       "      <td>1.457979</td>\n",
       "      <td>-4.087857</td>\n",
       "      <td>4.911091</td>\n",
       "      <td>-0.210420</td>\n",
       "      <td>1.489658</td>\n",
       "      <td>2.281600</td>\n",
       "      <td>2.656311</td>\n",
       "      <td>2.268567</td>\n",
       "      <td>-0.017489</td>\n",
       "      <td>1.340116</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>-1.786106</td>\n",
       "      <td>-4.123453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7750</th>\n",
       "      <td>-3.304127</td>\n",
       "      <td>-4.113443</td>\n",
       "      <td>-4.087857</td>\n",
       "      <td>-1.939757</td>\n",
       "      <td>-2.267499</td>\n",
       "      <td>-1.412018</td>\n",
       "      <td>-1.386643</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-1.758184</td>\n",
       "      <td>-2.082302</td>\n",
       "      <td>-0.911963</td>\n",
       "      <td>-0.845455</td>\n",
       "      <td>-2.358212</td>\n",
       "      <td>-4.123453</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  \\\n",
       "6175     -2.695272     1.457979         -4.087857  4.911091 -0.210420   \n",
       "7750     -3.304127    -4.113443         -4.087857 -1.939757 -2.267499   \n",
       "\n",
       "      LDAPS_CC1  LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4  \\\n",
       "6175   1.489658   2.281600   2.656311   2.268567   -0.017489    1.340116   \n",
       "7750  -1.412018  -1.386643  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "\n",
       "           lat       lon       DEM     Slope  Solar radiation  Next_Tmax  \n",
       "6175  1.189286 -0.005000  2.772243  1.115004        -1.786106  -4.123453  \n",
       "7750 -1.758184 -2.082302 -0.911963 -0.845455        -2.358212  -4.123453  "
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Y中小于箱型图下边缘的值\n",
    "Max_[Max_['Next_Tmax']<low_edge] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T02:24:40.455480Z",
     "start_time": "2020-06-16T02:24:40.448484Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6175, 7750], dtype=int64)"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 要删除的样本的Index\n",
    "drop_index=Max_[Max_['Next_Tmax']<low_edge].index.values\n",
    "drop_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T02:25:42.036217Z",
     "start_time": "2020-06-16T02:25:41.961262Z"
    }
   },
   "outputs": [],
   "source": [
    "# 删除Y中小于箱型图下边缘的值\n",
    "x=x.drop(drop_index)\n",
    "y=y.drop(drop_index) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T02:25:54.069979Z",
     "start_time": "2020-06-16T02:25:54.035982Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.361326</td>\n",
       "      <td>0.383078</td>\n",
       "      <td>-0.524889</td>\n",
       "      <td>-0.128382</td>\n",
       "      <td>0.206966</td>\n",
       "      <td>-0.516243</td>\n",
       "      <td>-0.592636</td>\n",
       "      <td>-0.629013</td>\n",
       "      <td>-0.664815</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>1.517935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.721084</td>\n",
       "      <td>0.311586</td>\n",
       "      <td>0.080895</td>\n",
       "      <td>-0.646994</td>\n",
       "      <td>-0.314841</td>\n",
       "      <td>-0.548557</td>\n",
       "      <td>-0.406199</td>\n",
       "      <td>-0.638055</td>\n",
       "      <td>-0.677462</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>0.511177</td>\n",
       "      <td>-0.315157</td>\n",
       "      <td>-0.542158</td>\n",
       "      <td>1.229950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.619608</td>\n",
       "      <td>-0.614982</td>\n",
       "      <td>0.162936</td>\n",
       "      <td>-0.441604</td>\n",
       "      <td>-1.249283</td>\n",
       "      <td>-0.610450</td>\n",
       "      <td>-0.384009</td>\n",
       "      <td>-0.458843</td>\n",
       "      <td>-0.620575</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>0.838510</td>\n",
       "      <td>-0.526218</td>\n",
       "      <td>-0.723133</td>\n",
       "      <td>1.216534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.754909</td>\n",
       "      <td>1.133054</td>\n",
       "      <td>0.031092</td>\n",
       "      <td>-0.666247</td>\n",
       "      <td>0.095997</td>\n",
       "      <td>-0.583539</td>\n",
       "      <td>-0.506548</td>\n",
       "      <td>-0.631178</td>\n",
       "      <td>-0.651696</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>0.385280</td>\n",
       "      <td>-0.297588</td>\n",
       "      <td>0.932424</td>\n",
       "      <td>1.201176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.551957</td>\n",
       "      <td>0.248765</td>\n",
       "      <td>-0.170325</td>\n",
       "      <td>-0.627154</td>\n",
       "      <td>1.354409</td>\n",
       "      <td>-0.832287</td>\n",
       "      <td>-0.413115</td>\n",
       "      <td>-0.559990</td>\n",
       "      <td>-0.510358</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>-0.494322</td>\n",
       "      <td>-0.548433</td>\n",
       "      <td>1.207205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>7746</th>\n",
       "      <td>-2.458495</td>\n",
       "      <td>-0.654605</td>\n",
       "      <td>-0.991760</td>\n",
       "      <td>-0.611932</td>\n",
       "      <td>0.585186</td>\n",
       "      <td>-1.157541</td>\n",
       "      <td>-1.291164</td>\n",
       "      <td>-1.278051</td>\n",
       "      <td>-1.112273</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.685654</td>\n",
       "      <td>1.191021</td>\n",
       "      <td>-0.735149</td>\n",
       "      <td>-0.820115</td>\n",
       "      <td>-2.096560</td>\n",
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       "    <tr>\n",
       "      <th>7747</th>\n",
       "      <td>-2.187892</td>\n",
       "      <td>-1.328126</td>\n",
       "      <td>-1.112066</td>\n",
       "      <td>-0.436683</td>\n",
       "      <td>0.284622</td>\n",
       "      <td>-1.297018</td>\n",
       "      <td>-1.071078</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.149390</td>\n",
       "      <td>-1.263971</td>\n",
       "      <td>-0.852681</td>\n",
       "      <td>-0.803915</td>\n",
       "      <td>-2.093040</td>\n",
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       "    <tr>\n",
       "      <th>7748</th>\n",
       "      <td>-2.187892</td>\n",
       "      <td>-1.548184</td>\n",
       "      <td>-0.887662</td>\n",
       "      <td>-0.255421</td>\n",
       "      <td>-0.454749</td>\n",
       "      <td>-1.274658</td>\n",
       "      <td>-1.094726</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-1.037356</td>\n",
       "      <td>-0.821213</td>\n",
       "      <td>-0.755095</td>\n",
       "      <td>-2.104553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7749</th>\n",
       "      <td>-2.221718</td>\n",
       "      <td>-1.555342</td>\n",
       "      <td>-0.570780</td>\n",
       "      <td>0.088072</td>\n",
       "      <td>-1.591397</td>\n",
       "      <td>-1.224577</td>\n",
       "      <td>-1.153504</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.178973</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-0.269384</td>\n",
       "      <td>-0.779043</td>\n",
       "      <td>-0.719338</td>\n",
       "      <td>-2.074325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7751</th>\n",
       "      <td>2.649126</td>\n",
       "      <td>1.624409</td>\n",
       "      <td>3.044561</td>\n",
       "      <td>6.792009</td>\n",
       "      <td>4.496044</td>\n",
       "      <td>2.291644</td>\n",
       "      <td>2.384303</td>\n",
       "      <td>2.670813</td>\n",
       "      <td>2.669002</td>\n",
       "      <td>11.935477</td>\n",
       "      <td>13.651790</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>2.861435</td>\n",
       "      <td>1.517935</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7750 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  \\\n",
       "0        -0.361326     0.383078         -0.524889 -0.128382  0.206966   \n",
       "1         0.721084     0.311586          0.080895 -0.646994 -0.314841   \n",
       "2         0.619608    -0.614982          0.162936 -0.441604 -1.249283   \n",
       "3         0.754909     1.133054          0.031092 -0.666247  0.095997   \n",
       "4         0.551957     0.248765         -0.170325 -0.627154  1.354409   \n",
       "...            ...          ...               ...       ...       ...   \n",
       "7746     -2.458495    -0.654605         -0.991760 -0.611932  0.585186   \n",
       "7747     -2.187892    -1.328126         -1.112066 -0.436683  0.284622   \n",
       "7748     -2.187892    -1.548184         -0.887662 -0.255421 -0.454749   \n",
       "7749     -2.221718    -1.555342         -0.570780  0.088072 -1.591397   \n",
       "7751      2.649126     1.624409          3.044561  6.792009  4.496044   \n",
       "\n",
       "      LDAPS_CC1  LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4  \\\n",
       "0     -0.516243  -0.592636  -0.629013  -0.664815   -0.305750   -0.224453   \n",
       "1     -0.548557  -0.406199  -0.638055  -0.677462   -0.305750   -0.224453   \n",
       "2     -0.610450  -0.384009  -0.458843  -0.620575   -0.305750   -0.224453   \n",
       "3     -0.583539  -0.506548  -0.631178  -0.651696   -0.305750   -0.224453   \n",
       "4     -0.832287  -0.413115  -0.559990  -0.510358   -0.305750   -0.224453   \n",
       "...         ...        ...        ...        ...         ...         ...   \n",
       "7746  -1.157541  -1.291164  -1.278051  -1.112273   -0.305750   -0.224453   \n",
       "7747  -1.297018  -1.071078  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "7748  -1.274658  -1.094726  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "7749  -1.224577  -1.153504  -1.278054  -1.178973   -0.305750   -0.224453   \n",
       "7751   2.291644   2.384303   2.670813   2.669002   11.935477   13.651790   \n",
       "\n",
       "           lat       lon       DEM     Slope  Solar radiation  \n",
       "0     1.189286 -0.005000  2.772243  1.115004         1.517935  \n",
       "1     1.189286  0.511177 -0.315157 -0.542158         1.229950  \n",
       "2     0.653021  0.838510 -0.526218 -0.723133         1.216534  \n",
       "3     1.991696  0.385280 -0.297588  0.932424         1.201176  \n",
       "4     0.118743  1.807917 -0.494322 -0.548433         1.207205  \n",
       "...        ...       ...       ...       ...              ...  \n",
       "7746 -0.685654  1.191021 -0.735149 -0.820115        -2.096560  \n",
       "7747 -0.149390 -1.263971 -0.852681 -0.803915        -2.093040  \n",
       "7748 -0.417522 -1.037356 -0.821213 -0.755095        -2.104553  \n",
       "7749 -0.417522 -0.269384 -0.779043 -0.719338        -2.074325  \n",
       "7751  1.991696  1.807917  2.772243  2.861435         1.517935  \n",
       "\n",
       "[7750 rows x 16 columns]"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0      -0.376282\n",
       "1       0.072097\n",
       "2       0.264260\n",
       "3       0.456422\n",
       "4       0.296287\n",
       "          ...   \n",
       "7746   -0.728580\n",
       "7747   -0.632499\n",
       "7748   -0.536418\n",
       "7749   -0.792634\n",
       "7751    2.762374\n",
       "Name: Next_Tmax, Length: 7750, dtype: float64"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 删除后的X,Y -------少了两行\n",
    "x\n",
    "y "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:21:08.371115Z",
     "start_time": "2020-06-16T03:21:08.337137Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.22952887192574348\n",
      "RMSE: 0.4790917155678477\n"
     ]
    }
   ],
   "source": [
    "# 重新划分数据集\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(x, y, test_size=0.3, random_state=1)\n",
    "# 对训练集进行训练\n",
    "lr = LinearRegression()\n",
    "lr.fit(X_train, Y_train) \n",
    "# 对测试集进行预测\n",
    "Y_pred = lr.predict(X_test)\n",
    "MSE = metrics.mean_squared_error(Y_test, Y_pred)\n",
    "RMSE = np.sqrt(metrics.mean_squared_error(Y_test, Y_pred)) \n",
    "print('MSE:',MSE) \n",
    "print('RMSE:',RMSE)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:27.726012Z",
     "start_time": "2020-06-16T00:15:27.652048Z"
    }
   },
   "outputs": [],
   "source": [
    "# 上一次评估结果\n",
    "# MSE: 0.25266554606897074\n",
    "# RMSE: 0.502658478560713  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:27.917423Z",
     "start_time": "2020-06-16T00:15:27.728011Z"
    }
   },
   "source": [
    "'''\n",
    "可以看到，该模型的RMSE由原来的0.5降低到了0.48，小幅度的提升了模型的准确率;去除异常值，提高准确度。\n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征筛选"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:27.527723Z",
     "start_time": "2020-06-16T03:22:27.496745Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
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       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.361326</td>\n",
       "      <td>0.383078</td>\n",
       "      <td>-0.524889</td>\n",
       "      <td>-0.128382</td>\n",
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       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>1.517935</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.721084</td>\n",
       "      <td>0.311586</td>\n",
       "      <td>0.080895</td>\n",
       "      <td>-0.646994</td>\n",
       "      <td>-0.314841</td>\n",
       "      <td>-0.548557</td>\n",
       "      <td>-0.406199</td>\n",
       "      <td>-0.638055</td>\n",
       "      <td>-0.677462</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>0.511177</td>\n",
       "      <td>-0.315157</td>\n",
       "      <td>-0.542158</td>\n",
       "      <td>1.229950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.619608</td>\n",
       "      <td>-0.614982</td>\n",
       "      <td>0.162936</td>\n",
       "      <td>-0.441604</td>\n",
       "      <td>-1.249283</td>\n",
       "      <td>-0.610450</td>\n",
       "      <td>-0.384009</td>\n",
       "      <td>-0.458843</td>\n",
       "      <td>-0.620575</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>0.838510</td>\n",
       "      <td>-0.526218</td>\n",
       "      <td>-0.723133</td>\n",
       "      <td>1.216534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.754909</td>\n",
       "      <td>1.133054</td>\n",
       "      <td>0.031092</td>\n",
       "      <td>-0.666247</td>\n",
       "      <td>0.095997</td>\n",
       "      <td>-0.583539</td>\n",
       "      <td>-0.506548</td>\n",
       "      <td>-0.631178</td>\n",
       "      <td>-0.651696</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>0.385280</td>\n",
       "      <td>-0.297588</td>\n",
       "      <td>0.932424</td>\n",
       "      <td>1.201176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.551957</td>\n",
       "      <td>0.248765</td>\n",
       "      <td>-0.170325</td>\n",
       "      <td>-0.627154</td>\n",
       "      <td>1.354409</td>\n",
       "      <td>-0.832287</td>\n",
       "      <td>-0.413115</td>\n",
       "      <td>-0.559990</td>\n",
       "      <td>-0.510358</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>-0.494322</td>\n",
       "      <td>-0.548433</td>\n",
       "      <td>1.207205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7746</th>\n",
       "      <td>-2.458495</td>\n",
       "      <td>-0.654605</td>\n",
       "      <td>-0.991760</td>\n",
       "      <td>-0.611932</td>\n",
       "      <td>0.585186</td>\n",
       "      <td>-1.157541</td>\n",
       "      <td>-1.291164</td>\n",
       "      <td>-1.278051</td>\n",
       "      <td>-1.112273</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.685654</td>\n",
       "      <td>1.191021</td>\n",
       "      <td>-0.735149</td>\n",
       "      <td>-0.820115</td>\n",
       "      <td>-2.096560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7747</th>\n",
       "      <td>-2.187892</td>\n",
       "      <td>-1.328126</td>\n",
       "      <td>-1.112066</td>\n",
       "      <td>-0.436683</td>\n",
       "      <td>0.284622</td>\n",
       "      <td>-1.297018</td>\n",
       "      <td>-1.071078</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.149390</td>\n",
       "      <td>-1.263971</td>\n",
       "      <td>-0.852681</td>\n",
       "      <td>-0.803915</td>\n",
       "      <td>-2.093040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7748</th>\n",
       "      <td>-2.187892</td>\n",
       "      <td>-1.548184</td>\n",
       "      <td>-0.887662</td>\n",
       "      <td>-0.255421</td>\n",
       "      <td>-0.454749</td>\n",
       "      <td>-1.274658</td>\n",
       "      <td>-1.094726</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-1.037356</td>\n",
       "      <td>-0.821213</td>\n",
       "      <td>-0.755095</td>\n",
       "      <td>-2.104553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7749</th>\n",
       "      <td>-2.221718</td>\n",
       "      <td>-1.555342</td>\n",
       "      <td>-0.570780</td>\n",
       "      <td>0.088072</td>\n",
       "      <td>-1.591397</td>\n",
       "      <td>-1.224577</td>\n",
       "      <td>-1.153504</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.178973</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-0.269384</td>\n",
       "      <td>-0.779043</td>\n",
       "      <td>-0.719338</td>\n",
       "      <td>-2.074325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7751</th>\n",
       "      <td>2.649126</td>\n",
       "      <td>1.624409</td>\n",
       "      <td>3.044561</td>\n",
       "      <td>6.792009</td>\n",
       "      <td>4.496044</td>\n",
       "      <td>2.291644</td>\n",
       "      <td>2.384303</td>\n",
       "      <td>2.670813</td>\n",
       "      <td>2.669002</td>\n",
       "      <td>11.935477</td>\n",
       "      <td>13.651790</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>2.861435</td>\n",
       "      <td>1.517935</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7750 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  \\\n",
       "0        -0.361326     0.383078         -0.524889 -0.128382  0.206966   \n",
       "1         0.721084     0.311586          0.080895 -0.646994 -0.314841   \n",
       "2         0.619608    -0.614982          0.162936 -0.441604 -1.249283   \n",
       "3         0.754909     1.133054          0.031092 -0.666247  0.095997   \n",
       "4         0.551957     0.248765         -0.170325 -0.627154  1.354409   \n",
       "...            ...          ...               ...       ...       ...   \n",
       "7746     -2.458495    -0.654605         -0.991760 -0.611932  0.585186   \n",
       "7747     -2.187892    -1.328126         -1.112066 -0.436683  0.284622   \n",
       "7748     -2.187892    -1.548184         -0.887662 -0.255421 -0.454749   \n",
       "7749     -2.221718    -1.555342         -0.570780  0.088072 -1.591397   \n",
       "7751      2.649126     1.624409          3.044561  6.792009  4.496044   \n",
       "\n",
       "      LDAPS_CC1  LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4  \\\n",
       "0     -0.516243  -0.592636  -0.629013  -0.664815   -0.305750   -0.224453   \n",
       "1     -0.548557  -0.406199  -0.638055  -0.677462   -0.305750   -0.224453   \n",
       "2     -0.610450  -0.384009  -0.458843  -0.620575   -0.305750   -0.224453   \n",
       "3     -0.583539  -0.506548  -0.631178  -0.651696   -0.305750   -0.224453   \n",
       "4     -0.832287  -0.413115  -0.559990  -0.510358   -0.305750   -0.224453   \n",
       "...         ...        ...        ...        ...         ...         ...   \n",
       "7746  -1.157541  -1.291164  -1.278051  -1.112273   -0.305750   -0.224453   \n",
       "7747  -1.297018  -1.071078  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "7748  -1.274658  -1.094726  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "7749  -1.224577  -1.153504  -1.278054  -1.178973   -0.305750   -0.224453   \n",
       "7751   2.291644   2.384303   2.670813   2.669002   11.935477   13.651790   \n",
       "\n",
       "           lat       lon       DEM     Slope  Solar radiation  \n",
       "0     1.189286 -0.005000  2.772243  1.115004         1.517935  \n",
       "1     1.189286  0.511177 -0.315157 -0.542158         1.229950  \n",
       "2     0.653021  0.838510 -0.526218 -0.723133         1.216534  \n",
       "3     1.991696  0.385280 -0.297588  0.932424         1.201176  \n",
       "4     0.118743  1.807917 -0.494322 -0.548433         1.207205  \n",
       "...        ...       ...       ...       ...              ...  \n",
       "7746 -0.685654  1.191021 -0.735149 -0.820115        -2.096560  \n",
       "7747 -0.149390 -1.263971 -0.852681 -0.803915        -2.093040  \n",
       "7748 -0.417522 -1.037356 -0.821213 -0.755095        -2.104553  \n",
       "7749 -0.417522 -0.269384 -0.779043 -0.719338        -2.074325  \n",
       "7751  1.991696  1.807917  2.772243  2.861435         1.517935  \n",
       "\n",
       "[7750 rows x 16 columns]"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:30.962802Z",
     "start_time": "2020-06-16T03:22:30.906836Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 特征筛选，选出和样本标签之间存在显著相关性（p<0.05)的特征，从而过滤掉对模型预测贡献不大的特征\n",
    "from sklearn.feature_selection import f_regression #回归问题用F检验做特征筛选,选出需要留下的特征数\n",
    "\n",
    "F,p = f_regression(x,y)  # 一个特征对应一个F值和p值\n",
    "k=F.shape[0] - (p>0.05).sum() # 去掉p值>0.05的特征，k就是剩下的特征数量\n",
    "k "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:33.930084Z",
     "start_time": "2020-06-16T03:22:33.914094Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7750, 14)"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[-0.36132577,  0.38307796, -0.52488856, ...,  1.18928568,\n",
       "         2.77224286,  1.11500407],\n",
       "       [ 0.72108401,  0.31158619,  0.08089519, ...,  1.18928568,\n",
       "        -0.31515742, -0.54215762],\n",
       "       [ 0.61960809, -0.61498177,  0.16293647, ...,  0.65302103,\n",
       "        -0.52621832, -0.7231326 ],\n",
       "       ...,\n",
       "       [-2.18789227, -1.54818379, -0.88766178, ..., -0.41752209,\n",
       "        -0.82121274, -0.75509512],\n",
       "       [-2.22171758, -1.55534234, -0.57077984, ..., -0.41752209,\n",
       "        -0.77904331, -0.71933797],\n",
       "       [ 2.64912642,  1.62440928,  3.04456067, ...,  1.99169648,\n",
       "         2.77224286,  2.86143459]])"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# F检验筛选特征\n",
    "from sklearn.feature_selection import SelectKBest #选择出存在显著相关性的特征\n",
    "\n",
    "X_f=SelectKBest(f_regression,k=k).fit_transform(x,y) \n",
    "X_f.shape    # 过滤掉了两个特征\n",
    "X_f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:38.343703Z",
     "start_time": "2020-06-16T03:22:38.336692Z"
    }
   },
   "outputs": [],
   "source": [
    "xtrain,xtest,ytrain,ytest=train_test_split(X_f,y,test_size=0.3, random_state=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:40.510995Z",
     "start_time": "2020-06-16T03:22:40.504012Z"
    }
   },
   "outputs": [],
   "source": [
    "# # 实例化并训练模型\n",
    "lr=LinearRegression().fit(xtrain,ytrain) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:45.574369Z",
     "start_time": "2020-06-16T03:22:45.562377Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7455182172970509, 0.759486375370554)"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 测试集上的评分（R²）\n",
    "lr.score(xtrain,ytrain),lr.score(xtest,ytest) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:50.126024Z",
     "start_time": "2020-06-16T03:22:50.117031Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.23443534187388376\n",
      "RMSE: 0.4841852350845529\n"
     ]
    }
   ],
   "source": [
    "ypred = lr.predict(xtest) \n",
    "MSE = metrics.mean_squared_error(ytest, ypred)\n",
    "RMSE = np.sqrt(metrics.mean_squared_error(ytest, ypred)) \n",
    "print('MSE:',MSE) \n",
    "print('RMSE:',RMSE) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCA(主成分分析)降维\n",
    "\n",
    "主成分分析的目的是从现有的特征中重建新的特征，新的特征剔除了原有特征中的冗余信息，因此更有区分度。\n",
    "\n",
    "主成分分析的结果是得到新的特征，而不是简单地舍弃原来的特征列表中的一些特征。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:58.610715Z",
     "start_time": "2020-06-16T03:22:58.587725Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca=PCA(n_components=0.97).fit(x,y) \n",
    "X_pca=pca.transform(x)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:04.873414Z",
     "start_time": "2020-06-16T03:23:04.832436Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
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       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.216577</td>\n",
       "      <td>3.103277</td>\n",
       "      <td>-0.110833</td>\n",
       "      <td>0.165686</td>\n",
       "      <td>0.790641</td>\n",
       "      <td>-0.681205</td>\n",
       "      <td>-1.317347</td>\n",
       "      <td>-0.238028</td>\n",
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       "      <td>0.603971</td>\n",
       "      <td>-0.337200</td>\n",
       "      <td>0.358007</td>\n",
       "      <td>-0.423250</td>\n",
       "      <td>1.099815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.090162</td>\n",
       "      <td>-0.107501</td>\n",
       "      <td>1.262260</td>\n",
       "      <td>0.415918</td>\n",
       "      <td>0.528656</td>\n",
       "      <td>-0.648804</td>\n",
       "      <td>-1.089290</td>\n",
       "      <td>-0.077244</td>\n",
       "      <td>-0.327796</td>\n",
       "      <td>0.455522</td>\n",
       "      <td>0.200883</td>\n",
       "      <td>-0.248300</td>\n",
       "      <td>-0.819938</td>\n",
       "      <td>0.253945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.170936</td>\n",
       "      <td>-0.890398</td>\n",
       "      <td>0.501055</td>\n",
       "      <td>0.423675</td>\n",
       "      <td>0.775087</td>\n",
       "      <td>-1.372693</td>\n",
       "      <td>-0.972450</td>\n",
       "      <td>0.227718</td>\n",
       "      <td>0.223689</td>\n",
       "      <td>0.190134</td>\n",
       "      <td>0.099512</td>\n",
       "      <td>-0.201590</td>\n",
       "      <td>-0.922498</td>\n",
       "      <td>0.184477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.834243</td>\n",
       "      <td>1.217238</td>\n",
       "      <td>1.663132</td>\n",
       "      <td>0.726037</td>\n",
       "      <td>0.587636</td>\n",
       "      <td>-0.459220</td>\n",
       "      <td>-0.996600</td>\n",
       "      <td>-0.280367</td>\n",
       "      <td>-0.815869</td>\n",
       "      <td>0.995221</td>\n",
       "      <td>0.419671</td>\n",
       "      <td>-0.486277</td>\n",
       "      <td>-0.841180</td>\n",
       "      <td>-0.672248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.214938</td>\n",
       "      <td>0.125820</td>\n",
       "      <td>1.699884</td>\n",
       "      <td>0.871620</td>\n",
       "      <td>0.306261</td>\n",
       "      <td>0.069075</td>\n",
       "      <td>-0.711520</td>\n",
       "      <td>-0.439632</td>\n",
       "      <td>-0.165456</td>\n",
       "      <td>-1.432835</td>\n",
       "      <td>-0.681152</td>\n",
       "      <td>-0.127686</td>\n",
       "      <td>-0.932863</td>\n",
       "      <td>0.190224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7745</th>\n",
       "      <td>-1.786437</td>\n",
       "      <td>0.231800</td>\n",
       "      <td>-0.034922</td>\n",
       "      <td>0.032411</td>\n",
       "      <td>-3.623162</td>\n",
       "      <td>-0.618894</td>\n",
       "      <td>0.170518</td>\n",
       "      <td>0.229131</td>\n",
       "      <td>1.534717</td>\n",
       "      <td>-1.291132</td>\n",
       "      <td>-0.853182</td>\n",
       "      <td>-0.036252</td>\n",
       "      <td>-0.137269</td>\n",
       "      <td>-0.045560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7746</th>\n",
       "      <td>-1.871831</td>\n",
       "      <td>-0.153308</td>\n",
       "      <td>-1.028628</td>\n",
       "      <td>-0.810918</td>\n",
       "      <td>-3.635659</td>\n",
       "      <td>0.140778</td>\n",
       "      <td>-0.260850</td>\n",
       "      <td>0.833126</td>\n",
       "      <td>0.884765</td>\n",
       "      <td>0.511711</td>\n",
       "      <td>-0.727532</td>\n",
       "      <td>0.529375</td>\n",
       "      <td>-0.279357</td>\n",
       "      <td>-0.127452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7747</th>\n",
       "      <td>-1.904300</td>\n",
       "      <td>-0.383570</td>\n",
       "      <td>-1.415379</td>\n",
       "      <td>-0.856730</td>\n",
       "      <td>-3.348666</td>\n",
       "      <td>-0.376563</td>\n",
       "      <td>-0.161274</td>\n",
       "      <td>0.946401</td>\n",
       "      <td>1.242176</td>\n",
       "      <td>0.431955</td>\n",
       "      <td>-0.515940</td>\n",
       "      <td>0.329307</td>\n",
       "      <td>-0.214308</td>\n",
       "      <td>-0.169483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7748</th>\n",
       "      <td>-1.829787</td>\n",
       "      <td>-0.559760</td>\n",
       "      <td>-1.535874</td>\n",
       "      <td>-0.621879</td>\n",
       "      <td>-2.943078</td>\n",
       "      <td>-1.374214</td>\n",
       "      <td>-0.052400</td>\n",
       "      <td>1.204855</td>\n",
       "      <td>1.792964</td>\n",
       "      <td>0.294714</td>\n",
       "      <td>-0.109837</td>\n",
       "      <td>-0.169241</td>\n",
       "      <td>-0.115426</td>\n",
       "      <td>-0.188458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7749</th>\n",
       "      <td>8.725825</td>\n",
       "      <td>3.056087</td>\n",
       "      <td>4.609578</td>\n",
       "      <td>4.595430</td>\n",
       "      <td>8.330166</td>\n",
       "      <td>9.435757</td>\n",
       "      <td>5.962135</td>\n",
       "      <td>0.194339</td>\n",
       "      <td>10.953023</td>\n",
       "      <td>3.910947</td>\n",
       "      <td>0.153466</td>\n",
       "      <td>1.686319</td>\n",
       "      <td>-0.332968</td>\n",
       "      <td>0.823091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7750 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2         3         4         5         6   \\\n",
       "0    -0.216577  3.103277 -0.110833  0.165686  0.790641 -0.681205 -1.317347   \n",
       "1    -1.090162 -0.107501  1.262260  0.415918  0.528656 -0.648804 -1.089290   \n",
       "2    -1.170936 -0.890398  0.501055  0.423675  0.775087 -1.372693 -0.972450   \n",
       "3    -0.834243  1.217238  1.663132  0.726037  0.587636 -0.459220 -0.996600   \n",
       "4    -1.214938  0.125820  1.699884  0.871620  0.306261  0.069075 -0.711520   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "7745 -1.786437  0.231800 -0.034922  0.032411 -3.623162 -0.618894  0.170518   \n",
       "7746 -1.871831 -0.153308 -1.028628 -0.810918 -3.635659  0.140778 -0.260850   \n",
       "7747 -1.904300 -0.383570 -1.415379 -0.856730 -3.348666 -0.376563 -0.161274   \n",
       "7748 -1.829787 -0.559760 -1.535874 -0.621879 -2.943078 -1.374214 -0.052400   \n",
       "7749  8.725825  3.056087  4.609578  4.595430  8.330166  9.435757  5.962135   \n",
       "\n",
       "            7          8         9         10        11        12        13  \n",
       "0    -0.238028  -0.203376  0.603971 -0.337200  0.358007 -0.423250  1.099815  \n",
       "1    -0.077244  -0.327796  0.455522  0.200883 -0.248300 -0.819938  0.253945  \n",
       "2     0.227718   0.223689  0.190134  0.099512 -0.201590 -0.922498  0.184477  \n",
       "3    -0.280367  -0.815869  0.995221  0.419671 -0.486277 -0.841180 -0.672248  \n",
       "4    -0.439632  -0.165456 -1.432835 -0.681152 -0.127686 -0.932863  0.190224  \n",
       "...        ...        ...       ...       ...       ...       ...       ...  \n",
       "7745  0.229131   1.534717 -1.291132 -0.853182 -0.036252 -0.137269 -0.045560  \n",
       "7746  0.833126   0.884765  0.511711 -0.727532  0.529375 -0.279357 -0.127452  \n",
       "7747  0.946401   1.242176  0.431955 -0.515940  0.329307 -0.214308 -0.169483  \n",
       "7748  1.204855   1.792964  0.294714 -0.109837 -0.169241 -0.115426 -0.188458  \n",
       "7749  0.194339  10.953023  3.910947  0.153466  1.686319 -0.332968  0.823091  \n",
       "\n",
       "[7750 rows x 14 columns]"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_pca=pd.DataFrame(X_pca)\n",
    "X_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:07.297511Z",
     "start_time": "2020-06-16T03:23:07.288516Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      -0.376282\n",
       "1       0.072097\n",
       "2       0.264260\n",
       "3       0.456422\n",
       "4       0.296287\n",
       "          ...   \n",
       "7746   -0.728580\n",
       "7747   -0.632499\n",
       "7748   -0.536418\n",
       "7749   -0.792634\n",
       "7751    2.762374\n",
       "Name: Next_Tmax, Length: 7750, dtype: float64"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:10.305510Z",
     "start_time": "2020-06-16T03:23:10.277525Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
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       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
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       "      <th>11</th>\n",
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       "      <th>13</th>\n",
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       "      <th>15</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>-0.228159</td>\n",
       "      <td>0.259139</td>\n",
       "      <td>-0.363300</td>\n",
       "      <td>0.214156</td>\n",
       "      <td>-0.098169</td>\n",
       "      <td>0.389183</td>\n",
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       "      <td>0.162411</td>\n",
       "      <td>0.025265</td>\n",
       "      <td>-0.008065</td>\n",
       "      <td>0.076506</td>\n",
       "      <td>0.069054</td>\n",
       "      <td>0.108524</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.177762</td>\n",
       "      <td>0.259298</td>\n",
       "      <td>-0.115402</td>\n",
       "      <td>0.139626</td>\n",
       "      <td>0.253892</td>\n",
       "      <td>-0.080516</td>\n",
       "      <td>-0.148532</td>\n",
       "      <td>-0.188108</td>\n",
       "      <td>-0.188080</td>\n",
       "      <td>0.000994</td>\n",
       "      <td>-0.060552</td>\n",
       "      <td>0.175085</td>\n",
       "      <td>0.061905</td>\n",
       "      <td>0.573748</td>\n",
       "      <td>0.580675</td>\n",
       "      <td>0.008013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.041856</td>\n",
       "      <td>0.375808</td>\n",
       "      <td>0.133046</td>\n",
       "      <td>-0.096357</td>\n",
       "      <td>0.346909</td>\n",
       "      <td>0.204690</td>\n",
       "      <td>0.055840</td>\n",
       "      <td>-0.139376</td>\n",
       "      <td>-0.192081</td>\n",
       "      <td>0.314158</td>\n",
       "      <td>-0.091826</td>\n",
       "      <td>0.459231</td>\n",
       "      <td>0.361806</td>\n",
       "      <td>-0.282789</td>\n",
       "      <td>-0.231722</td>\n",
       "      <td>0.160499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.128714</td>\n",
       "      <td>-0.109038</td>\n",
       "      <td>-0.004835</td>\n",
       "      <td>-0.019345</td>\n",
       "      <td>0.086365</td>\n",
       "      <td>-0.269335</td>\n",
       "      <td>-0.083357</td>\n",
       "      <td>0.229271</td>\n",
       "      <td>0.328601</td>\n",
       "      <td>-0.366989</td>\n",
       "      <td>0.459058</td>\n",
       "      <td>0.392905</td>\n",
       "      <td>0.459881</td>\n",
       "      <td>0.010425</td>\n",
       "      <td>0.048139</td>\n",
       "      <td>-0.099735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.521499</td>\n",
       "      <td>-0.132052</td>\n",
       "      <td>0.415585</td>\n",
       "      <td>0.200768</td>\n",
       "      <td>-0.109847</td>\n",
       "      <td>0.064168</td>\n",
       "      <td>0.077508</td>\n",
       "      <td>0.086216</td>\n",
       "      <td>0.099563</td>\n",
       "      <td>0.174102</td>\n",
       "      <td>0.008157</td>\n",
       "      <td>-0.070494</td>\n",
       "      <td>0.084456</td>\n",
       "      <td>0.188751</td>\n",
       "      <td>0.208160</td>\n",
       "      <td>0.580179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.234427</td>\n",
       "      <td>0.185108</td>\n",
       "      <td>-0.007308</td>\n",
       "      <td>0.228682</td>\n",
       "      <td>0.630832</td>\n",
       "      <td>-0.071018</td>\n",
       "      <td>-0.105731</td>\n",
       "      <td>-0.007234</td>\n",
       "      <td>0.102064</td>\n",
       "      <td>0.046327</td>\n",
       "      <td>0.409067</td>\n",
       "      <td>-0.213486</td>\n",
       "      <td>-0.445201</td>\n",
       "      <td>-0.111096</td>\n",
       "      <td>-0.084715</td>\n",
       "      <td>-0.031756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.377125</td>\n",
       "      <td>-0.085739</td>\n",
       "      <td>0.078499</td>\n",
       "      <td>0.002232</td>\n",
       "      <td>0.043436</td>\n",
       "      <td>0.071895</td>\n",
       "      <td>0.110212</td>\n",
       "      <td>0.108762</td>\n",
       "      <td>0.049060</td>\n",
       "      <td>0.444529</td>\n",
       "      <td>-0.093587</td>\n",
       "      <td>-0.110817</td>\n",
       "      <td>0.207857</td>\n",
       "      <td>0.087366</td>\n",
       "      <td>0.163401</td>\n",
       "      <td>-0.717383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.013714</td>\n",
       "      <td>-0.274986</td>\n",
       "      <td>-0.115457</td>\n",
       "      <td>0.835690</td>\n",
       "      <td>-0.034486</td>\n",
       "      <td>-0.002497</td>\n",
       "      <td>-0.048047</td>\n",
       "      <td>-0.071086</td>\n",
       "      <td>-0.092055</td>\n",
       "      <td>-0.053476</td>\n",
       "      <td>-0.223025</td>\n",
       "      <td>0.311199</td>\n",
       "      <td>-0.028485</td>\n",
       "      <td>-0.119224</td>\n",
       "      <td>-0.142026</td>\n",
       "      <td>-0.103987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.370291</td>\n",
       "      <td>-0.141150</td>\n",
       "      <td>0.121759</td>\n",
       "      <td>0.177218</td>\n",
       "      <td>-0.215333</td>\n",
       "      <td>-0.003833</td>\n",
       "      <td>-0.205263</td>\n",
       "      <td>-0.251031</td>\n",
       "      <td>-0.129045</td>\n",
       "      <td>0.434367</td>\n",
       "      <td>0.584006</td>\n",
       "      <td>-0.194281</td>\n",
       "      <td>0.238188</td>\n",
       "      <td>0.003956</td>\n",
       "      <td>-0.061626</td>\n",
       "      <td>0.022683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.042747</td>\n",
       "      <td>-0.017327</td>\n",
       "      <td>0.098680</td>\n",
       "      <td>-0.178946</td>\n",
       "      <td>-0.295307</td>\n",
       "      <td>-0.123886</td>\n",
       "      <td>-0.067586</td>\n",
       "      <td>0.010266</td>\n",
       "      <td>0.076458</td>\n",
       "      <td>0.310972</td>\n",
       "      <td>0.161651</td>\n",
       "      <td>0.616572</td>\n",
       "      <td>-0.574300</td>\n",
       "      <td>0.036589</td>\n",
       "      <td>0.062666</td>\n",
       "      <td>-0.070417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.228455</td>\n",
       "      <td>0.371271</td>\n",
       "      <td>0.185369</td>\n",
       "      <td>0.123466</td>\n",
       "      <td>-0.328695</td>\n",
       "      <td>0.234673</td>\n",
       "      <td>0.281510</td>\n",
       "      <td>-0.140353</td>\n",
       "      <td>-0.417383</td>\n",
       "      <td>-0.404841</td>\n",
       "      <td>0.328316</td>\n",
       "      <td>0.009849</td>\n",
       "      <td>-0.060252</td>\n",
       "      <td>-0.034421</td>\n",
       "      <td>0.054214</td>\n",
       "      <td>-0.211079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.034273</td>\n",
       "      <td>-0.637297</td>\n",
       "      <td>-0.063050</td>\n",
       "      <td>-0.208273</td>\n",
       "      <td>0.323071</td>\n",
       "      <td>0.465238</td>\n",
       "      <td>0.257615</td>\n",
       "      <td>-0.075044</td>\n",
       "      <td>-0.237589</td>\n",
       "      <td>-0.093466</td>\n",
       "      <td>0.184750</td>\n",
       "      <td>0.156335</td>\n",
       "      <td>-0.079597</td>\n",
       "      <td>0.142051</td>\n",
       "      <td>0.039583</td>\n",
       "      <td>0.025827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-0.352815</td>\n",
       "      <td>0.057000</td>\n",
       "      <td>0.671374</td>\n",
       "      <td>0.083665</td>\n",
       "      <td>0.102315</td>\n",
       "      <td>0.319565</td>\n",
       "      <td>-0.077398</td>\n",
       "      <td>-0.062434</td>\n",
       "      <td>0.416761</td>\n",
       "      <td>-0.181899</td>\n",
       "      <td>-0.123503</td>\n",
       "      <td>0.028882</td>\n",
       "      <td>-0.074246</td>\n",
       "      <td>0.140435</td>\n",
       "      <td>-0.075031</td>\n",
       "      <td>-0.195759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.048350</td>\n",
       "      <td>0.064477</td>\n",
       "      <td>0.013980</td>\n",
       "      <td>0.004915</td>\n",
       "      <td>0.015767</td>\n",
       "      <td>-0.187600</td>\n",
       "      <td>0.140001</td>\n",
       "      <td>0.123015</td>\n",
       "      <td>-0.138519</td>\n",
       "      <td>0.039964</td>\n",
       "      <td>0.012322</td>\n",
       "      <td>0.013607</td>\n",
       "      <td>0.012477</td>\n",
       "      <td>0.666108</td>\n",
       "      <td>-0.676660</td>\n",
       "      <td>-0.016238</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5         6   \\\n",
       "0  -0.228159  0.259139 -0.363300  0.214156 -0.098169  0.389183  0.420652   \n",
       "1  -0.177762  0.259298 -0.115402  0.139626  0.253892 -0.080516 -0.148532   \n",
       "2   0.041856  0.375808  0.133046 -0.096357  0.346909  0.204690  0.055840   \n",
       "3   0.128714 -0.109038 -0.004835 -0.019345  0.086365 -0.269335 -0.083357   \n",
       "4   0.521499 -0.132052  0.415585  0.200768 -0.109847  0.064168  0.077508   \n",
       "5   0.234427  0.185108 -0.007308  0.228682  0.630832 -0.071018 -0.105731   \n",
       "6   0.377125 -0.085739  0.078499  0.002232  0.043436  0.071895  0.110212   \n",
       "7   0.013714 -0.274986 -0.115457  0.835690 -0.034486 -0.002497 -0.048047   \n",
       "8  -0.370291 -0.141150  0.121759  0.177218 -0.215333 -0.003833 -0.205263   \n",
       "9   0.042747 -0.017327  0.098680 -0.178946 -0.295307 -0.123886 -0.067586   \n",
       "10  0.228455  0.371271  0.185369  0.123466 -0.328695  0.234673  0.281510   \n",
       "11 -0.034273 -0.637297 -0.063050 -0.208273  0.323071  0.465238  0.257615   \n",
       "12 -0.352815  0.057000  0.671374  0.083665  0.102315  0.319565 -0.077398   \n",
       "13  0.048350  0.064477  0.013980  0.004915  0.015767 -0.187600  0.140001   \n",
       "\n",
       "          7         8         9         10        11        12        13  \\\n",
       "0   0.400378  0.343841  0.192053  0.162411  0.025265 -0.008065  0.076506   \n",
       "1  -0.188108 -0.188080  0.000994 -0.060552  0.175085  0.061905  0.573748   \n",
       "2  -0.139376 -0.192081  0.314158 -0.091826  0.459231  0.361806 -0.282789   \n",
       "3   0.229271  0.328601 -0.366989  0.459058  0.392905  0.459881  0.010425   \n",
       "4   0.086216  0.099563  0.174102  0.008157 -0.070494  0.084456  0.188751   \n",
       "5  -0.007234  0.102064  0.046327  0.409067 -0.213486 -0.445201 -0.111096   \n",
       "6   0.108762  0.049060  0.444529 -0.093587 -0.110817  0.207857  0.087366   \n",
       "7  -0.071086 -0.092055 -0.053476 -0.223025  0.311199 -0.028485 -0.119224   \n",
       "8  -0.251031 -0.129045  0.434367  0.584006 -0.194281  0.238188  0.003956   \n",
       "9   0.010266  0.076458  0.310972  0.161651  0.616572 -0.574300  0.036589   \n",
       "10 -0.140353 -0.417383 -0.404841  0.328316  0.009849 -0.060252 -0.034421   \n",
       "11 -0.075044 -0.237589 -0.093466  0.184750  0.156335 -0.079597  0.142051   \n",
       "12 -0.062434  0.416761 -0.181899 -0.123503  0.028882 -0.074246  0.140435   \n",
       "13  0.123015 -0.138519  0.039964  0.012322  0.013607  0.012477  0.666108   \n",
       "\n",
       "          14        15  \n",
       "0   0.069054  0.108524  \n",
       "1   0.580675  0.008013  \n",
       "2  -0.231722  0.160499  \n",
       "3   0.048139 -0.099735  \n",
       "4   0.208160  0.580179  \n",
       "5  -0.084715 -0.031756  \n",
       "6   0.163401 -0.717383  \n",
       "7  -0.142026 -0.103987  \n",
       "8  -0.061626  0.022683  \n",
       "9   0.062666 -0.070417  \n",
       "10  0.054214 -0.211079  \n",
       "11  0.039583  0.025827  \n",
       "12 -0.075031 -0.195759  \n",
       "13 -0.676660 -0.016238  "
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "components=pca.components_   #14个新特征的特征向量(每一行) \n",
    "components=pd.DataFrame(components) \n",
    "components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:51.506451Z",
     "start_time": "2020-06-16T03:23:51.497454Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.25542858, 0.12862968, 0.08850255, 0.08191585, 0.07267401,\n",
       "       0.06801911, 0.05608564, 0.05187105, 0.05015169, 0.04070604,\n",
       "       0.0339251 , 0.02304823, 0.01785588, 0.01306852])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_ #主成分的解释方差百分比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:54.442229Z",
     "start_time": "2020-06-16T03:23:54.432222Z"
    }
   },
   "outputs": [],
   "source": [
    "# 重新划分训练集和测试集\n",
    "Xtrain,Xtest,Ytrain,Ytest=train_test_split(X_pca,y, test_size=0.3, random_state=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:57.857425Z",
     "start_time": "2020-06-16T03:23:57.848431Z"
    }
   },
   "outputs": [],
   "source": [
    "# # 实例化并训练模型\n",
    "lr=LinearRegression().fit(Xtrain,Ytrain) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:59.855182Z",
     "start_time": "2020-06-16T03:23:59.839193Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7383072663535954, 0.7383072663535954)"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 模型在训练集和测试机上的评分（R²）\n",
    "lr.score(Xtrain,Ytrain),lr.score(Xtrain,Ytrain)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 随机森林回归 RandomForestRegression（20分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:36:37.843504Z",
     "start_time": "2020-06-16T00:15:30.474184Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=None,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'n_estimators': range(1, 200, 10), 'max_depth': range(1, 10)},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1.使用集成算法-随机森林回归器\n",
    "# 2.使用网格搜索寻找主要参数的最优取值组合\n",
    "# 3.使用去除异常值后的训练集数据训练模型（15分）\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(x, y, test_size=0.3, random_state=1)\n",
    "rfr = RandomForestRegressor()\n",
    "p = {\n",
    "    'n_estimators':range(1,200,10),\n",
    "    'max_depth':range(1,10)\n",
    "}\n",
    "GS = GridSearchCV(estimator=rfr,param_grid=p,cv=5)\n",
    "GS.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:39:36.195735Z",
     "start_time": "2020-06-16T00:39:36.191739Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 9, 'n_estimators': 161}"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看最优参数组合\n",
    "GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:40:27.950412Z",
     "start_time": "2020-06-16T00:40:22.666442Z"
    }
   },
   "outputs": [],
   "source": [
    "# 用上面得到的最优参数组合重新实例化模型\n",
    "rfr=RandomForestRegressor(n_estimators=161,\n",
    "                         max_depth=9,).fit(X_train,Y_train)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:40:30.380572Z",
     "start_time": "2020-06-16T00:40:30.207691Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9136367959928572, 0.8575226375048766)"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfr.score(X_train,Y_train),rfr.score(X_test,Y_test)   # 随机森林回归算法的score返回的是R²，并不是mse(尽管实例化时用的criterion='mse') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:40:48.947100Z",
     "start_time": "2020-06-16T00:40:39.499061Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.16385047035287673"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # 使用交叉验证---随机森林回归器在测试集上的MSE平均得分（5分）\n",
    "-cross_val_score(rfr,X_test,Y_test,scoring='neg_mean_squared_error',cv=5).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:36:54.809803Z",
     "start_time": "2020-06-16T00:36:54.803807Z"
    },
    "scrolled": false
   },
   "source": [
    "'''\n",
    "可以看出：无论是在训练集上还是测试集上，随机森林的的表现均优于LinearRegression,Lasson和Ridge：\n",
    "训练集上的MSE由原来的0.25降到了0.15，R²由原来的0.75上升到了0.85。\n",
    "'''"
   ]
  }
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